Table of Contents
Pipeline Execution Summary
{
"start_time": "2025-06-07T12:28:47.359189",
"arguments": {
"target_dir": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples",
"output_dir": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/output",
"recursive": true,
"skip_steps": "",
"only_steps": "",
"verbose": true,
"strict": false,
"estimate_resources": true,
"ontology_terms_file": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/ontology/act_inf_ontology_terms.json",
"llm_tasks": "all",
"llm_timeout": 60,
"pipeline_summary_file": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/output/pipeline_execution_summary.json",
"site_html_filename": "gnn_pipeline_summary_site.html",
"discopy_gnn_input_dir": null,
"discopy_jax_gnn_input_dir": null,
"discopy_jax_seed": 0,
"recreate_venv": false,
"dev": false
},
"steps": [
{
"step_number": 1,
"script_name": "1_gnn.py",
"status": "SUCCESS",
"start_time": "2025-06-07T12:28:47.361076",
"end_time": "2025-06-07T12:28:47.413239",
"duration_seconds": 0.052163,
"details": "",
"stdout": "2025-06-07 12:28:47,399 - __main__ - DEBUG - Script logger '__main__' level set to DEBUG.\n2025-06-07 12:28:47,399 - __main__ - DEBUG - Project root determined for path relativization: /home/trim/Documents/GitHub/GeneralizedNotationNotation\n2025-06-07 12:28:47,400 - __main__ - INFO - GNN Step 1: Target directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples\n2025-06-07 12:28:47,400 - __main__ - INFO - GNN Step 1: Output directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output\n2025-06-07 12:28:47,400 - __main__ - INFO - GNN Step 1: Recursive: True\n2025-06-07 12:28:47,400 - __main__ - INFO - GNN Step 1: Verbose: True\n2025-06-07 12:28:47,400 - __main__ - INFO - Starting GNN file processing for directory: 'src/gnn/examples'\n2025-06-07 12:28:47,400 - __main__ - INFO - Recursive mode enabled: searching in subdirectories.\n2025-06-07 12:28:47,400 - __main__ - DEBUG - Ensured output directory exists: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_processing_step\n2025-06-07 12:28:47,400 - __main__ - DEBUG - Searching for GNN files matching pattern '**/*.md' in '/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples'\n2025-06-07 12:28:47,400 - __main__ - INFO - Found 2 .md file(s) to process in 'src/gnn/examples'.\n2025-06-07 12:28:47,400 - __main__ - DEBUG - Processing file: src/gnn/examples/pymdp_pomdp_agent.md\n2025-06-07 12:28:47,401 - __main__ - DEBUG - Successfully read content from src/gnn/examples/pymdp_pomdp_agent.md.\n2025-06-07 12:28:47,401 - __main__ - DEBUG - Found '## ModelName' header in src/gnn/examples/pymdp_pomdp_agent.md\n2025-06-07 12:28:47,401 - __main__ - DEBUG - Extracted ModelName: 'Multifactor PyMDP Agent v1' from src/gnn/examples/pymdp_pomdp_agent.md\n2025-06-07 12:28:47,401 - __main__ - DEBUG - Found StateSpaceBlock section in src/gnn/examples/pymdp_pomdp_agent.md\n2025-06-07 12:28:47,401 - __main__ - DEBUG - Found Connections section in src/gnn/examples/pymdp_pomdp_agent.md\n2025-06-07 12:28:47,401 - __main__ - DEBUG - Found ModelParameters section in src/gnn/examples/pymdp_pomdp_agent.md\n2025-06-07 12:28:47,401 - __main__ - DEBUG - Parsed ModelParameter: num_hidden_states_factors = [2, 3]\n2025-06-07 12:28:47,401 - __main__ - DEBUG - Parsed ModelParameter: num_obs_modalities = [3, 3, 3]\n2025-06-07 12:28:47,401 - __main__ - DEBUG - Parsed ModelParameter: num_control_factors = [1, 3]\n2025-06-07 12:28:47,401 - __main__ - DEBUG - Processing file: src/gnn/examples/rxinfer_multiagent_gnn.md\n2025-06-07 12:28:47,402 - __main__ - DEBUG - Successfully read content from src/gnn/examples/rxinfer_multiagent_gnn.md.\n2025-06-07 12:28:47,402 - __main__ - DEBUG - Found '## ModelName' header in src/gnn/examples/rxinfer_multiagent_gnn.md\n2025-06-07 12:28:47,402 - __main__ - DEBUG - Extracted ModelName: 'Multi-agent Trajectory Planning' from src/gnn/examples/rxinfer_multiagent_gnn.md\n2025-06-07 12:28:47,402 - __main__ - DEBUG - Found StateSpaceBlock section in src/gnn/examples/rxinfer_multiagent_gnn.md\n2025-06-07 12:28:47,402 - __main__ - DEBUG - Found Connections section in src/gnn/examples/rxinfer_multiagent_gnn.md\n2025-06-07 12:28:47,402 - __main__ - DEBUG - Found ModelParameters section in src/gnn/examples/rxinfer_multiagent_gnn.md\n2025-06-07 12:28:47,402 - __main__ - INFO - GNN discovery report saved to: output/gnn_processing_step/1_gnn_discovery_report.md\n2025-06-07 12:28:47,402 - __main__ - INFO - Step 1_gnn completed successfully.\n2025-06-07 12:28:47,402 - __main__ - INFO - Step 1_gnn completed successfully.\n",
"stderr": ""
},
{
"step_number": 2,
"script_name": "2_setup.py",
"status": "SUCCESS",
"start_time": "2025-06-07T12:28:47.413286",
"end_time": "2025-06-07T12:28:49.969615",
"duration_seconds": 2.556329,
"details": "",
"stdout": "2025-06-07 12:28:47,456 - __main__ - DEBUG - Script logger '__main__' level set to DEBUG.\n2025-06-07 12:28:47,456 - __main__ - INFO - \u25b6\ufe0f Starting Step 2: Setup\n2025-06-07 12:28:47,456 - __main__ - DEBUG - Parsed arguments for setup: Namespace(target_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples'), output_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/output'), verbose=True, recreate_venv=False, dev=False)\n2025-06-07 12:28:47,456 - __main__ - INFO - \ud83d\udcca Gathering environment information...\n2025-06-07 12:28:47,456 - __main__ - INFO - --- Environment Information ---\n2025-06-07 12:28:47,456 - __main__ - INFO - Python version: 3.12.2 | packaged by conda-forge | (main, Feb 16 2024, 20:50:58) [GCC 12.3.0]\n2025-06-07 12:28:47,456 - __main__ - INFO - Python executable: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv/bin/python\n2025-06-07 12:28:47,460 - __main__ - INFO - Operating system: Linux-6.12.10-76061203-generic-x86_64-with-glibc2.35\n2025-06-07 12:28:47,460 - __main__ - DEBUG - Checking virtual environment status...\n2025-06-07 12:28:47,460 - __main__ - INFO - Virtual environment: Active at /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv\n2025-06-07 12:28:47,460 - __main__ - INFO - - Python: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv/bin/python\n2025-06-07 12:28:47,460 - __main__ - INFO - - Pip: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv/bin/pip\n2025-06-07 12:28:47,460 - __main__ - DEBUG - Checking system dependencies...\n2025-06-07 12:28:47,647 - __main__ - INFO - System dependencies:\n2025-06-07 12:28:47,647 - __main__ - INFO - - pip: \u2713 Available\n2025-06-07 12:28:47,647 - __main__ - INFO - - venv: \u2713 Available\n2025-06-07 12:28:47,647 - __main__ - INFO - - graphviz: \u2717 Not found\n2025-06-07 12:28:47,647 - __main__ - INFO - -----------------------------\n2025-06-07 12:28:47,647 - __main__ - DEBUG - Phase 1: Verifying project directories...\n2025-06-07 12:28:47,648 - __main__ - INFO - \ud83d\udd0d Verifying directories...\n2025-06-07 12:28:47,648 - __main__ - DEBUG - Searching for GNN files in /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples...\n2025-06-07 12:28:47,648 - __main__ - DEBUG - Found 0 GNN .md files (recursively in target: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples)\n2025-06-07 12:28:47,648 - __main__ - WARNING - \u26a0\ufe0f Warning: No GNN files found in /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples. This might be expected if you're planning to create them later.\n2025-06-07 12:28:47,648 - __main__ - INFO - \ud83d\udcc1 Creating output directory structure...\n2025-06-07 12:28:47,649 - __main__ - INFO - \u2705 Output directory structure verified/created: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output\n2025-06-07 12:28:47,649 - __main__ - DEBUG - \ud83d\udcc4 Directory structure info saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/directory_structure.json\n2025-06-07 12:28:47,649 - __main__ - INFO - \u2705 Project directories verified successfully.\n2025-06-07 12:28:47,649 - __main__ - INFO - Phase 2: Setting up Python virtual environment and dependencies...\n2025-06-07 12:28:47,649 - __main__ - INFO - \u23f3 This may take a few minutes, especially if installing dependencies...\n2025-06-07 12:28:47,649 - __main__ - DEBUG - \ud83d\udc0d Calling perform_full_setup from src/setup/setup.py\n2025-06-07 12:28:47,649 - __main__ - INFO - \ud83d\udce6 Setup config: recreate_venv=False, install_dev=False\n2025-06-07 12:28:47,649 - INFO - \ud83d\ude80 Starting environment setup...\n2025-06-07 12:28:47,649 - INFO - \ud83d\udcc1 Project root: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src\n2025-06-07 12:28:47,649 - INFO - \u2699\ufe0f Configuration: verbose=True, recreate_venv=False, dev=False\n2025-06-07 12:28:47,649 - INFO - \n\ud83d\udccb Phase 1/3: Checking system requirements...\n2025-06-07 12:28:47,649 - INFO - \ud83d\udd0d Checking system requirements...\n2025-06-07 12:28:47,649 - INFO - \u2705 Python version check passed: 3.12.2\n2025-06-07 12:28:47,649 - DEBUG - Checking pip availability...\n2025-06-07 12:28:47,649 - DEBUG - Running command: '/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv/bin/python -m pip --version' in /home/trim/Documents/GitHub/GeneralizedNotationNotation/src\n2025-06-07 12:28:47,793 - DEBUG - Stdout:\npip 25.1.1 from /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv/lib/python3.12/site-packages/pip (python 3.12)\n2025-06-07 12:28:47,793 - INFO - \u2705 pip is available: pip 25.1.1 from /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv/lib/python3.12/site-packages/pip (python 3.12)\n2025-06-07 12:28:47,793 - DEBUG - Checking venv module availability...\n2025-06-07 12:28:47,793 - DEBUG - Running command: '/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv/bin/python -c import venv' in /home/trim/Documents/GitHub/GeneralizedNotationNotation/src\n2025-06-07 12:28:47,823 - INFO - \u2705 venv module is available\n2025-06-07 12:28:47,823 - INFO - \u2705 Disk space check passed: 61.19GB free\n2025-06-07 12:28:47,823 - INFO - \u2705 System requirements check passed\n2025-06-07 12:28:47,823 - INFO - \n\ud83d\udccb Phase 2/3: Setting up virtual environment...\n2025-06-07 12:28:47,823 - INFO - \u2713 Using existing virtual environment at /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv\n2025-06-07 12:28:47,823 - INFO - \u2705 Virtual environment setup completed in 0.0s\n2025-06-07 12:28:47,823 - INFO - \n\ud83d\udccb Phase 3/3: Installing dependencies...\n2025-06-07 12:28:47,823 - INFO - \u23f3 This may take several minutes...\n2025-06-07 12:28:47,823 - INFO - \ud83d\udce6 Installing dependencies from /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt...\n2025-06-07 12:28:47,823 - INFO - \ud83d\udce6 Upgrading pip in virtual environment...\n2025-06-07 12:28:48,163 - DEBUG - Requirement already satisfied: pip in ./.venv/lib/python3.12/site-packages (25.1.1)\n2025-06-07 12:28:48,560 - INFO - \ud83d\udce6 Installing main dependencies (this may take several minutes)...\n2025-06-07 12:28:48,934 - DEBUG - Requirement already satisfied: graphviz>=0.20.1 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 2)) (0.20.3)\n2025-06-07 12:28:48,935 - DEBUG - Requirement already satisfied: matplotlib>=3.7.1 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 3)) (3.10.3)\n2025-06-07 12:28:48,935 - DEBUG - Requirement already satisfied: networkx>=3.1 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 4)) (3.5)\n2025-06-07 12:28:48,935 - DEBUG - Requirement already satisfied: numpy>=1.24.3 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 5)) (2.2.6)\n2025-06-07 12:28:48,936 - DEBUG - Requirement already satisfied: pandas>=2.0.1 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 6)) (2.2.3)\n2025-06-07 12:28:48,936 - DEBUG - Requirement already satisfied: pytest>=7.3.1 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 7)) (8.3.5)\n2025-06-07 12:28:48,936 - DEBUG - Requirement already satisfied: scipy>=1.10.1 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 8)) (1.15.3)\n2025-06-07 12:28:48,936 - DEBUG - Requirement already satisfied: inferactively-pymdp<0.1.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 11)) (0.0.7.1)\n2025-06-07 12:28:48,937 - DEBUG - Requirement already satisfied: pydantic>=2.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 14)) (2.11.4)\n2025-06-07 12:28:48,937 - DEBUG - Requirement already satisfied: openai>=1.0.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 17)) (1.79.0)\n2025-06-07 12:28:48,937 - DEBUG - Requirement already satisfied: python-dotenv>=1.0.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 18)) (1.1.0)\n2025-06-07 12:28:48,938 - DEBUG - Requirement already satisfied: Markdown>=3.4.3 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 21)) (3.8)\n2025-06-07 12:28:48,938 - DEBUG - Requirement already satisfied: httpx>=0.27.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 24)) (0.28.1)\n2025-06-07 12:28:48,938 - DEBUG - Requirement already satisfied: altair>=5.0.1 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 27)) (5.5.0)\n2025-06-07 12:28:48,939 - DEBUG - Requirement already satisfied: discopy>=1.0.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 28)) (1.2.0)\n2025-06-07 12:28:48,939 - DEBUG - Requirement already satisfied: jax>=0.4.20 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 31)) (0.6.0)\n2025-06-07 12:28:48,939 - DEBUG - Requirement already satisfied: jaxlib>=0.4.20 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 32)) (0.6.0)\n2025-06-07 12:28:48,940 - DEBUG - Requirement already satisfied: pytest-cov>=4.1.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 36)) (6.1.1)\n2025-06-07 12:28:48,940 - DEBUG - Requirement already satisfied: pytest-mock>=3.10.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 37)) (3.14.1)\n2025-06-07 12:28:48,940 - DEBUG - Requirement already satisfied: flake8>=6.0.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 40)) (7.2.0)\n2025-06-07 12:28:48,941 - DEBUG - Requirement already satisfied: mypy>=1.3.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 41)) (1.16.0)\n2025-06-07 12:28:48,941 - DEBUG - Requirement already satisfied: black>=23.3.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 42)) (25.1.0)\n2025-06-07 12:28:48,941 - DEBUG - Requirement already satisfied: isort>=5.12.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 43)) (6.0.1)\n2025-06-07 12:28:48,942 - DEBUG - Requirement already satisfied: sphinx>=7.0.1 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 46)) (8.2.3)\n2025-06-07 12:28:48,942 - DEBUG - Requirement already satisfied: sphinx-rtd-theme>=1.2.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 47)) (3.0.2)\n2025-06-07 12:28:48,942 - DEBUG - Requirement already satisfied: ipython>=8.13.2 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 50)) (9.2.0)\n2025-06-07 12:28:48,942 - DEBUG - Requirement already satisfied: ipdb>=0.13.13 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 51)) (0.13.13)\n2025-06-07 12:28:48,943 - DEBUG - Requirement already satisfied: types-requests>=2.30.0.0 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 54)) (2.32.0.20250602)\n2025-06-07 12:28:48,943 - DEBUG - Requirement already satisfied: types-Markdown>=3.4.2.9 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 55)) (3.8.0.20250415)\n2025-06-07 12:28:48,943 - DEBUG - Requirement already satisfied: py-spy>=0.3.14 in ./.venv/lib/python3.12/site-packages (from -r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 58)) (0.4.0)\n2025-06-07 12:28:48,945 - DEBUG - Requirement already satisfied: attrs>=20.3.0 in ./.venv/lib/python3.12/site-packages (from inferactively-pymdp<0.1.0->-r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 11)) (25.3.0)\n2025-06-07 12:28:48,945 - DEBUG - Requirement already satisfied: cycler>=0.10.0 in ./.venv/lib/python3.12/site-packages (from inferactively-pymdp<0.1.0->-r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 11)) (0.12.1)\n2025-06-07 12:28:48,945 - 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DEBUG - Requirement already satisfied: asttokens>=2.1.0 in ./.venv/lib/python3.12/site-packages (from stack_data->ipython>=8.13.2->-r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 50)) (3.0.0)\n2025-06-07 12:28:49,304 - DEBUG - Requirement already satisfied: pure-eval in ./.venv/lib/python3.12/site-packages (from stack_data->ipython>=8.13.2->-r /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/requirements.txt (line 50)) (0.2.3)\n2025-06-07 12:28:49,479 - INFO - \u2705 Dependencies installation completed\n2025-06-07 12:28:49,479 - INFO - \u2705 All dependencies installed successfully (took 0.9s)\n2025-06-07 12:28:49,479 - INFO - \ud83d\udccb Getting list of installed packages...\n2025-06-07 12:28:49,734 - INFO - \ud83d\udce6 Found 138 installed packages in the virtual environment\n2025-06-07 12:28:49,734 - INFO - \ud83d\udccb Installed packages:\n2025-06-07 12:28:49,735 - INFO - - Jinja2: 3.1.6\n2025-06-07 12:28:49,735 - INFO - - Markdown: 3.8\n2025-06-07 12:28:49,735 - INFO - - MarkupSafe: 3.0.2\n2025-06-07 12:28:49,735 - INFO - - PyYAML: 6.0.2\n2025-06-07 12:28:49,735 - INFO - - Pygments: 2.19.1\n2025-06-07 12:28:49,735 - INFO - - SQLAlchemy: 2.0.41\n2025-06-07 12:28:49,735 - INFO - - Sphinx: 8.2.3\n2025-06-07 12:28:49,735 - INFO - - XlsxWriter: 3.2.3\n2025-06-07 12:28:49,735 - INFO - - alabaster: 1.0.0\n2025-06-07 12:28:49,735 - INFO - - altair: 5.5.0\n2025-06-07 12:28:49,735 - INFO - - annotated-types: 0.7.0\n2025-06-07 12:28:49,735 - INFO - - anyio: 4.9.0\n2025-06-07 12:28:49,735 - INFO - - asttokens: 3.0.0\n2025-06-07 12:28:49,735 - INFO - - attrs: 25.3.0\n2025-06-07 12:28:49,735 - INFO - - autograd: 1.8.0\n2025-06-07 12:28:49,735 - INFO - - babel: 2.17.0\n2025-06-07 12:28:49,735 - INFO - - black: 25.1.0\n2025-06-07 12:28:49,735 - INFO - - certifi: 2025.4.26\n2025-06-07 12:28:49,735 - INFO - - charset-normalizer: 3.4.2\n2025-06-07 12:28:49,735 - INFO - - click: 8.2.1\n2025-06-07 12:28:49,735 - INFO - - comm: 0.2.2\n2025-06-07 12:28:49,735 - INFO - - contourpy: 1.3.2\n2025-06-07 12:28:49,735 - INFO - - coverage: 7.8.2\n2025-06-07 12:28:49,735 - INFO - - cycler: 0.12.1\n2025-06-07 12:28:49,735 - INFO - - debugpy: 1.8.14\n2025-06-07 12:28:49,735 - INFO - - decorator: 5.2.1\n2025-06-07 12:28:49,735 - INFO - - discopy: 1.2.0\n2025-06-07 12:28:49,735 - INFO - - distro: 1.9.0\n2025-06-07 12:28:49,735 - INFO - - docutils: 0.21.2\n2025-06-07 12:28:49,735 - INFO - - et_xmlfile: 2.0.0\n2025-06-07 12:28:49,735 - INFO - - executing: 2.2.0\n2025-06-07 12:28:49,735 - INFO - - fastjsonschema: 2.21.1\n2025-06-07 12:28:49,735 - INFO - - flake8: 7.2.0\n2025-06-07 12:28:49,735 - INFO - - fonttools: 4.58.2\n2025-06-07 12:28:49,735 - INFO - - graphviz: 0.20.3\n2025-06-07 12:28:49,735 - INFO - - greenlet: 3.2.2\n2025-06-07 12:28:49,735 - INFO - - h11: 0.16.0\n2025-06-07 12:28:49,735 - INFO - - httpcore: 1.0.9\n2025-06-07 12:28:49,735 - INFO - - httpx: 0.28.1\n2025-06-07 12:28:49,735 - INFO - - idna: 3.10\n2025-06-07 12:28:49,735 - INFO - - imagesize: 1.4.1\n2025-06-07 12:28:49,735 - INFO - - importlib_metadata: 8.7.0\n2025-06-07 12:28:49,735 - INFO - - inferactively-pymdp: 0.0.7.1\n2025-06-07 12:28:49,735 - INFO - - iniconfig: 2.1.0\n2025-06-07 12:28:49,735 - INFO - - ipdb: 0.13.13\n2025-06-07 12:28:49,735 - INFO - - ipykernel: 6.29.5\n2025-06-07 12:28:49,735 - INFO - - ipython: 9.2.0\n2025-06-07 12:28:49,735 - INFO - - ipython_pygments_lexers: 1.1.1\n2025-06-07 12:28:49,735 - INFO - - isort: 6.0.1\n2025-06-07 12:28:49,735 - INFO - - jax: 0.6.0\n2025-06-07 12:28:49,735 - INFO - - jaxlib: 0.6.0\n2025-06-07 12:28:49,735 - INFO - - jedi: 0.19.2\n2025-06-07 12:28:49,735 - INFO - - jiter: 0.10.0\n2025-06-07 12:28:49,735 - INFO - - jsonschema: 4.23.0\n2025-06-07 12:28:49,735 - INFO - - jsonschema-specifications: 2025.4.1\n2025-06-07 12:28:49,735 - INFO - - jupyter-cache: 1.0.1\n2025-06-07 12:28:49,736 - INFO - - jupyter_client: 8.6.3\n2025-06-07 12:28:49,736 - INFO - - jupyter_core: 5.7.2\n2025-06-07 12:28:49,736 - INFO - - kiwisolver: 1.4.8\n2025-06-07 12:28:49,736 - INFO - - markdown-it-py: 3.0.0\n2025-06-07 12:28:49,736 - INFO - - matplotlib: 3.10.3\n2025-06-07 12:28:49,736 - INFO - - matplotlib-inline: 0.1.7\n2025-06-07 12:28:49,736 - INFO - - mccabe: 0.7.0\n2025-06-07 12:28:49,736 - INFO - - mdit-py-plugins: 0.4.2\n2025-06-07 12:28:49,736 - INFO - - mdurl: 0.1.2\n2025-06-07 12:28:49,736 - INFO - - ml_dtypes: 0.5.1\n2025-06-07 12:28:49,736 - INFO - - mypy: 1.16.0\n2025-06-07 12:28:49,736 - INFO - - mypy_extensions: 1.1.0\n2025-06-07 12:28:49,736 - INFO - - myst-nb: 1.2.0\n2025-06-07 12:28:49,736 - INFO - - myst-parser: 4.0.1\n2025-06-07 12:28:49,736 - INFO - - narwhals: 1.40.0\n2025-06-07 12:28:49,736 - INFO - - nbclient: 0.10.2\n2025-06-07 12:28:49,736 - INFO - - nbformat: 5.10.4\n2025-06-07 12:28:49,736 - INFO - - nest-asyncio: 1.6.0\n2025-06-07 12:28:49,736 - INFO - - networkx: 3.5\n2025-06-07 12:28:49,736 - INFO - - nose: 1.3.7\n2025-06-07 12:28:49,736 - INFO - - numpy: 2.2.6\n2025-06-07 12:28:49,736 - INFO - - openai: 1.79.0\n2025-06-07 12:28:49,736 - INFO - - openpyxl: 3.1.5\n2025-06-07 12:28:49,736 - INFO - - opt_einsum: 3.4.0\n2025-06-07 12:28:49,736 - INFO - - packaging: 25.0\n2025-06-07 12:28:49,736 - INFO - - pandas: 2.2.3\n2025-06-07 12:28:49,736 - INFO - - parso: 0.8.4\n2025-06-07 12:28:49,736 - INFO - - pathspec: 0.12.1\n2025-06-07 12:28:49,736 - INFO - - pexpect: 4.9.0\n2025-06-07 12:28:49,736 - INFO - - pillow: 11.2.1\n2025-06-07 12:28:49,736 - INFO - - pip: 25.1.1\n2025-06-07 12:28:49,736 - INFO - - platformdirs: 4.3.8\n2025-06-07 12:28:49,736 - INFO - - pluggy: 1.6.0\n2025-06-07 12:28:49,736 - INFO - - prompt_toolkit: 3.0.51\n2025-06-07 12:28:49,736 - INFO - - psutil: 7.0.0\n2025-06-07 12:28:49,736 - INFO - - ptyprocess: 0.7.0\n2025-06-07 12:28:49,736 - INFO - - pure_eval: 0.2.3\n2025-06-07 12:28:49,736 - INFO - - py: 1.11.0\n2025-06-07 12:28:49,736 - INFO - - py-spy: 0.4.0\n2025-06-07 12:28:49,736 - INFO - - pycodestyle: 2.13.0\n2025-06-07 12:28:49,736 - INFO - - pydantic: 2.11.4\n2025-06-07 12:28:49,736 - INFO - - pydantic_core: 2.33.2\n2025-06-07 12:28:49,736 - 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INFO - - sphinxcontrib-applehelp: 2.0.0\n2025-06-07 12:28:49,736 - INFO - - sphinxcontrib-devhelp: 2.0.0\n2025-06-07 12:28:49,736 - INFO - - sphinxcontrib-htmlhelp: 2.1.0\n2025-06-07 12:28:49,736 - INFO - - sphinxcontrib-jquery: 4.1\n2025-06-07 12:28:49,736 - INFO - - sphinxcontrib-jsmath: 1.0.1\n2025-06-07 12:28:49,736 - INFO - - sphinxcontrib-qthelp: 2.0.0\n2025-06-07 12:28:49,736 - INFO - - sphinxcontrib-serializinghtml: 2.0.0\n2025-06-07 12:28:49,736 - INFO - - stack-data: 0.6.3\n2025-06-07 12:28:49,736 - INFO - - tabulate: 0.9.0\n2025-06-07 12:28:49,736 - INFO - - toml: 0.10.2\n2025-06-07 12:28:49,736 - INFO - - tornado: 6.5\n2025-06-07 12:28:49,737 - INFO - - tqdm: 4.67.1\n2025-06-07 12:28:49,737 - INFO - - traitlets: 5.14.3\n2025-06-07 12:28:49,737 - INFO - - types-Markdown: 3.8.0.20250415\n2025-06-07 12:28:49,737 - INFO - - types-requests: 2.32.0.20250602\n2025-06-07 12:28:49,737 - INFO - - typing-inspection: 0.4.0\n2025-06-07 12:28:49,737 - INFO - - typing_extensions: 4.13.2\n2025-06-07 12:28:49,737 - INFO - - tzdata: 2025.2\n2025-06-07 12:28:49,737 - INFO - - urllib3: 2.4.0\n2025-06-07 12:28:49,737 - INFO - - wcwidth: 0.2.13\n2025-06-07 12:28:49,737 - INFO - - zipp: 3.21.0\n2025-06-07 12:28:49,737 - INFO - \ud83d\udcc4 Full package list saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv/installed_packages.json\n2025-06-07 12:28:49,737 - INFO - \u2705 Dependencies installed in 1.9s\n2025-06-07 12:28:49,737 - INFO - \n\ud83c\udf89 Setup completed successfully!\n2025-06-07 12:28:49,737 - INFO - \u23f1\ufe0f Total time: 2.1s\n2025-06-07 12:28:49,737 - INFO - \nTo activate the virtual environment:\n2025-06-07 12:28:49,737 - INFO - source .venv/bin/activate\n2025-06-07 12:28:49,737 - __main__ - INFO - \u2705 Python virtual environment and dependencies setup completed.\n2025-06-07 12:28:49,737 - __main__ - INFO - Phase 3: Generating installed packages report...\n2025-06-07 12:28:49,737 - __main__ - INFO - \ud83d\udccb Generating installed packages report...\n2025-06-07 12:28:49,737 - __main__ - DEBUG - Running /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv/bin/pip list --format=json\n2025-06-07 12:28:49,950 - __main__ - INFO - \ud83d\udce6 Found 138 installed packages in the virtual environment\n2025-06-07 12:28:49,950 - __main__ - INFO - Installed packages:\n2025-06-07 12:28:49,950 - __main__ - INFO - - Jinja2: 3.1.6\n2025-06-07 12:28:49,950 - __main__ - INFO - - Markdown: 3.8\n2025-06-07 12:28:49,950 - __main__ - INFO - - MarkupSafe: 3.0.2\n2025-06-07 12:28:49,950 - __main__ - INFO - - PyYAML: 6.0.2\n2025-06-07 12:28:49,950 - __main__ - INFO - - Pygments: 2.19.1\n2025-06-07 12:28:49,950 - __main__ - INFO - - SQLAlchemy: 2.0.41\n2025-06-07 12:28:49,950 - __main__ - INFO - - Sphinx: 8.2.3\n2025-06-07 12:28:49,950 - __main__ - INFO - - XlsxWriter: 3.2.3\n2025-06-07 12:28:49,950 - __main__ - INFO - - alabaster: 1.0.0\n2025-06-07 12:28:49,950 - __main__ - INFO - - altair: 5.5.0\n2025-06-07 12:28:49,950 - __main__ - INFO - - annotated-types: 0.7.0\n2025-06-07 12:28:49,950 - 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__main__ - INFO - - wcwidth: 0.2.13\n2025-06-07 12:28:49,952 - __main__ - INFO - - zipp: 3.21.0\n2025-06-07 12:28:49,952 - __main__ - INFO - \ud83d\udcc4 Package list saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/installed_packages.json\n2025-06-07 12:28:49,953 - __main__ - INFO - \u2705 Package report completed.\n2025-06-07 12:28:49,953 - __main__ - INFO - \u2705 Step 2: Setup complete (took 2.5 seconds)\n",
"stderr": ""
},
{
"step_number": 4,
"script_name": "4_gnn_type_checker.py",
"status": "SUCCESS",
"start_time": "2025-06-07T12:28:49.969672",
"end_time": "2025-06-07T12:28:51.843299",
"duration_seconds": 1.873627,
"details": "",
"stdout": "2025-06-07 12:28:50,470 - __main__ - INFO - \u25b6\ufe0f Starting Step 4: Type Checking (4_gnn_type_checker.py)...\n2025-06-07 12:28:50,470 - __main__ - DEBUG - Set logger '__main__' to DEBUG within run_type_checker.\n2025-06-07 12:28:50,470 - __main__ - DEBUG - Set logger 'gnn_type_checker' to INFO.\n2025-06-07 12:28:50,471 - __main__ - INFO - \ud83d\udc0d Invoking GNN Type Checker module with arguments: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples --output-dir /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_type_check --report-file type_check_report.md --project-root /home/trim/Documents/GitHub/GeneralizedNotationNotation --recursive --estimate-resources\n2025-06-07 12:28:50,471 - __main__ - INFO - \u2139\ufe0f Target GNN files in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples\n2025-06-07 12:28:50,471 - __main__ - INFO - \u2139\ufe0f Type checker outputs will be in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_type_check\n2025-06-07 12:28:50,471 - __main__ - INFO - \ud83d\udcdd Main type check report will be: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_type_check/type_check_report.md\n2025-06-07 12:28:51,771 - __main__ - INFO - \u2705 GNN Type Checker module completed successfully.\n2025-06-07 12:28:51,771 - __main__ - INFO - \u2705 Step 4: Type Checking complete.\n",
"stderr": ""
},
{
"step_number": 5,
"script_name": "5_export.py",
"status": "SUCCESS",
"start_time": "2025-06-07T12:28:51.843341",
"end_time": "2025-06-07T12:28:52.036688",
"duration_seconds": 0.193347,
"details": "",
"stdout": "2025-06-07 12:28:52,000 - __main__ - INFO - --- 5_export.py execution started ---\n2025-06-07 12:28:52,000 - __main__ - INFO - Received arguments: Namespace(target_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples'), output_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/output'), recursive=True, formats='json,xml,txt_summary,dsl', verbose=True)\n2025-06-07 12:28:52,000 - __main__ - INFO - Resolved target directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples\n2025-06-07 12:28:52,000 - __main__ - INFO - Checking if target directory exists: True\n2025-06-07 12:28:52,000 - __main__ - INFO - Checking if target directory is a directory: True\n2025-06-07 12:28:52,000 - __main__ - INFO - Recursive mode: True\n2025-06-07 12:28:52,000 - __main__ - DEBUG - Export script started with arguments: Namespace(target_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples'), output_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/output'), recursive=True, formats='json,xml,txt_summary,dsl', verbose=True)\n2025-06-07 12:28:52,000 - __main__ - INFO - Using glob pattern: '**/*.md' in directory '/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples'\n2025-06-07 12:28:52,000 - __main__ - INFO - Found 2 files using glob pattern.\n2025-06-07 12:28:52,000 - __main__ - DEBUG - Discovered files list:\n2025-06-07 12:28:52,000 - __main__ - DEBUG - - /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/pymdp_pomdp_agent.md\n2025-06-07 12:28:52,000 - __main__ - DEBUG - - /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md\n2025-06-07 12:28:52,000 - __main__ - INFO - Starting GNN export for 2 discovered GNN files...\n2025-06-07 12:28:52,000 - __main__ - INFO - Will attempt to export to the following formats: json, xml, txt_summary, dsl\n2025-06-07 12:28:52,000 - __main__ - INFO - Processing exports for GNN file: src/gnn/examples/pymdp_pomdp_agent.md\n2025-06-07 12:28:52,002 - __main__ - DEBUG - \ud83d\udce4 Ensured export subdirectory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_exports/pymdp_pomdp_agent\n2025-06-07 12:28:52,002 - __main__ - DEBUG - \ud83d\udce4 Exporting to JSON -> output/gnn_exports/pymdp_pomdp_agent/pymdp_pomdp_agent.json\n2025-06-07 12:28:52,003 - __main__ - INFO - \u2705 Successfully exported to pymdp_pomdp_agent.json (format: json, size: 14.91 KB)\n2025-06-07 12:28:52,003 - __main__ - DEBUG - \ud83d\udce4 Exporting to XML -> output/gnn_exports/pymdp_pomdp_agent/pymdp_pomdp_agent.xml\n2025-06-07 12:28:52,005 - __main__ - INFO - \u2705 Successfully exported to pymdp_pomdp_agent.xml (format: xml, size: 14.03 KB)\n2025-06-07 12:28:52,005 - __main__ - DEBUG - \ud83d\udce4 Exporting to TXT_SUMMARY -> output/gnn_exports/pymdp_pomdp_agent/pymdp_pomdp_agent.txt\n2025-06-07 12:28:52,005 - __main__ - INFO - \u2705 Successfully exported to pymdp_pomdp_agent.txt (format: txt_summary, size: 3.01 KB)\n2025-06-07 12:28:52,006 - __main__ - DEBUG - \ud83d\udce4 Exporting to DSL -> output/gnn_exports/pymdp_pomdp_agent/pymdp_pomdp_agent.gnn\n2025-06-07 12:28:52,006 - __main__ - INFO - \u2705 Successfully exported to pymdp_pomdp_agent.gnn (format: dsl, size: 5.96 KB)\n2025-06-07 12:28:52,006 - __main__ - INFO - \u2705 All 4 requested exports for src/gnn/examples/pymdp_pomdp_agent.md completed successfully.\n2025-06-07 12:28:52,006 - __main__ - INFO - Processing exports for GNN file: src/gnn/examples/rxinfer_multiagent_gnn.md\n2025-06-07 12:28:52,008 - __main__ - DEBUG - \ud83d\udce4 Ensured export subdirectory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_exports/rxinfer_multiagent_gnn\n2025-06-07 12:28:52,008 - __main__ - DEBUG - \ud83d\udce4 Exporting to JSON -> output/gnn_exports/rxinfer_multiagent_gnn/rxinfer_multiagent_gnn.json\n2025-06-07 12:28:52,009 - __main__ - INFO - \u2705 Successfully exported to rxinfer_multiagent_gnn.json (format: json, size: 23.64 KB)\n2025-06-07 12:28:52,009 - __main__ - DEBUG - \ud83d\udce4 Exporting to XML -> output/gnn_exports/rxinfer_multiagent_gnn/rxinfer_multiagent_gnn.xml\n2025-06-07 12:28:52,012 - __main__ - INFO - \u2705 Successfully exported to rxinfer_multiagent_gnn.xml (format: xml, size: 28.48 KB)\n2025-06-07 12:28:52,012 - __main__ - DEBUG - \ud83d\udce4 Exporting to TXT_SUMMARY -> output/gnn_exports/rxinfer_multiagent_gnn/rxinfer_multiagent_gnn.txt\n2025-06-07 12:28:52,012 - __main__ - INFO - \u2705 Successfully exported to rxinfer_multiagent_gnn.txt (format: txt_summary, size: 8.40 KB)\n2025-06-07 12:28:52,012 - __main__ - DEBUG - \ud83d\udce4 Exporting to DSL -> output/gnn_exports/rxinfer_multiagent_gnn/rxinfer_multiagent_gnn.gnn\n2025-06-07 12:28:52,013 - __main__ - INFO - \u2705 Successfully exported to rxinfer_multiagent_gnn.gnn (format: dsl, size: 8.51 KB)\n2025-06-07 12:28:52,013 - __main__ - INFO - \u2705 All 4 requested exports for src/gnn/examples/rxinfer_multiagent_gnn.md completed successfully.\n2025-06-07 12:28:52,013 - __main__ - INFO - \ud83d\udcc4 Generating GNN Export Step Summary Report...\n2025-06-07 12:28:52,013 - __main__ - INFO - \ud83d\udcc4 Export step summary report generated: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_exports/5_export_step_report.md\n2025-06-07 12:28:52,013 - __main__ - INFO - \u2705 GNN export step finished. 2 files fully exported, 0 had issues.\n",
"stderr": "Could not parse matrix string with ast.literal_eval: 'false'. Error: malformed node or string on line 1: . Returning as raw string.\n"
},
{
"step_number": 6,
"script_name": "6_visualization.py",
"status": "SUCCESS",
"start_time": "2025-06-07T12:28:52.036726",
"end_time": "2025-06-07T12:29:07.393920",
"duration_seconds": 15.357194,
"details": "",
"stdout": "2025-06-07 12:28:52,531 - __main__ - INFO - \u25b6\ufe0f Starting Step 6: Visualization (6_visualization.py)\n2025-06-07 12:28:52,531 - __main__ - DEBUG - Parsing options:\n2025-06-07 12:28:52,531 - __main__ - DEBUG - Target directory/file: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples\n2025-06-07 12:28:52,531 - __main__ - DEBUG - Pipeline output directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output\n2025-06-07 12:28:52,531 - __main__ - DEBUG - Recursive: True\n2025-06-07 12:28:52,531 - __main__ - DEBUG - Verbose: True\n2025-06-07 12:28:52,531 - __main__ - INFO - \ud83d\uddbc\ufe0f Preparing to generate GNN visualizations...\n2025-06-07 12:28:52,531 - __main__ - DEBUG - \ud83c\udfaf Target GNN files in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples\n2025-06-07 12:28:52,531 - __main__ - DEBUG - \u0565\u056c Output visualizations will be in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization\n2025-06-07 12:28:52,531 - __main__ - DEBUG - \ud83d\udd04 Recursive mode: Enabled\n2025-06-07 12:28:52,531 - __main__ - DEBUG - \ud83d\udc0d Invoking GNN Visualization module (visualization.cli.main)\n2025-06-07 12:28:52,532 - __main__ - DEBUG - Arguments: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples --output-dir /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization --project-root /home/trim/Documents/GitHub/GeneralizedNotationNotation --recursive\nChecking for variables in /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/pymdp_pomdp_agent.md...\nFound 21 variables: ['A_m0', 'A_m1', 'A_m2', 'B_f0', 'B_f1', 'C_m0', 'C_m1', 'C_m2', 'D_f0', 'D_f1', 's_f0', 's_f1', 's_prime_f0', 's_prime_f1', 'o_m0', 'o_m1', 'o_m2', '\u03c0_f1', 'u_f1', 'G', 't']\nState space visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/state_space.png\nChecking for edges in /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/pymdp_pomdp_agent.md...\nFound 2 edges\nConnections visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/connections.png\n[GNNVisualizer] Found 'InitialParameterization' section for pymdp_pomdp_agent. Attempting matrix visualization.\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/matrix_Am0.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/matrix_Am1.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/matrix_Am2.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/matrix_Bf0.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/matrix_Bf1.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/matrix_Cm0.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/matrix_Cm1.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/matrix_Cm2.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/matrix_Df0.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/matrix_Df1.png\nCombined matrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/combined_matrices.png\n[GNNVisualizer] Found 'ActInfOntologyAnnotation' section for pymdp_pomdp_agent. Attempting ontology visualization.\nOntology visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/ontology_annotations.png\nCombined visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/pymdp_pomdp_agent/combined_visualization.png\nChecking for variables in /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md...\nFound 60 variables: ['dt', 'gamma', 'nr_steps', 'nr_iterations', 'nr_agents', 'softmin_temperature', 'intermediate_steps', 'save_intermediates', 'A', 'B', 'C', 'initial_state_variance', 'control_variance', 'goal_constraint_variance', 'gamma_shape', 'gamma_scale_factor', 'x_limits', 'y_limits', 'fps', 'heatmap_resolution', 'plot_width', 'plot_height', 'agent_alpha', 'target_alpha', 'color_palette', 'door_obstacle_center_1', 'door_obstacle_size_1', 'door_obstacle_center_2', 'door_obstacle_size_2', 'wall_obstacle_center', 'wall_obstacle_size', 'combined_obstacle_center_1', 'combined_obstacle_size_1', 'combined_obstacle_center_2', 'combined_obstacle_size_2', 'combined_obstacle_center_3', 'combined_obstacle_size_3', 'agent1_id', 'agent1_radius', 'agent1_initial_position', 'agent1_target_position', 'agent2_id', 'agent2_radius', 'agent2_initial_position', 'agent2_target_position', 'agent3_id', 'agent3_radius', 'agent3_initial_position', 'agent3_target_position', 'agent4_id', 'agent4_radius', 'agent4_initial_position', 'agent4_target_position', 'experiment_seeds', 'results_dir', 'animation_template', 'control_vis_filename', 'obstacle_distance_filename', 'path_uncertainty_filename', 'convergence_filename']\nState space visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/state_space.png\nChecking for edges in /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md...\nFound 1 edges\nConnections visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/connections.png\n[GNNVisualizer] Found 'InitialParameterization' section for rxinfer_multiagent_gnn. Attempting matrix visualization.\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_A.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_B.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_C.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_xlimits.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_ylimits.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_doorobstaclecenter1.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_doorobstaclesize1.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_doorobstaclecenter2.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_doorobstaclesize2.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_wallobstaclecenter.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_wallobstaclesize.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_combinedobstaclecenter1.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_combinedobstaclesize1.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_combinedobstaclecenter2.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_combinedobstaclesize2.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_combinedobstaclecenter3.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_combinedobstaclesize3.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_agent1initialposition.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_agent1targetposition.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_agent2initialposition.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_agent2targetposition.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_agent3initialposition.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_agent3targetposition.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_agent4initialposition.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_agent4targetposition.png\nMatrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/matrix_experimentseeds.png\nCombined matrix visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/combined_matrices.png\n[GNNVisualizer] Found 'ActInfOntologyAnnotation' section for rxinfer_multiagent_gnn. Attempting ontology visualization.\nOntology visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/ontology_annotations.png\nCombined visualization saved to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization/rxinfer_multiagent_gnn/combined_visualization.png\nVisualizations generated in /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization\n2025-06-07 12:29:07,268 - __main__ - INFO - \u2705 GNN Visualization module completed successfully.\n2025-06-07 12:29:07,268 - __main__ - DEBUG - \ud83d\uddbc\ufe0f Visualizations should be available in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_examples_visualization\n2025-06-07 12:29:07,268 - __main__ - DEBUG - \ud83d\udcca Found 54 items (files/directories) in the output directory.\n2025-06-07 12:29:07,268 - __main__ - INFO - \u2705 Step 6: Visualization (6_visualization.py) - COMPLETED\n",
"stderr": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/visualization/matrix_visualizer.py:247: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n plt.tight_layout() # Make room for colorbar and title\n/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/visualization/matrix_visualizer.py:247: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n plt.tight_layout() # Make room for colorbar and title\n"
},
{
"step_number": 7,
"script_name": "7_mcp.py",
"status": "SUCCESS",
"start_time": "2025-06-07T12:29:07.393961",
"end_time": "2025-06-07T12:29:08.962234",
"duration_seconds": 1.568273,
"details": "",
"stdout": "2025-06-07 12:29:08,655 - __main__ - DEBUG - Verbose logging enabled for standalone run of 7_mcp.py, including core MCP modules.\n2025-06-07 12:29:08,655 - __main__ - DEBUG - Script logger '__main__' level set to DEBUG.\n2025-06-07 12:29:08,655 - __main__ - INFO - \u25b6\ufe0f Starting Step 7: MCP Operations (7_mcp.py)\n2025-06-07 12:29:08,655 - __main__ - DEBUG - Parsing options (from main.py or standalone):\n2025-06-07 12:29:08,656 - __main__ - DEBUG - Effective MCP Core Directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/mcp\n2025-06-07 12:29:08,656 - __main__ - DEBUG - Effective Project Source Root (for module scanning): /home/trim/Documents/GitHub/GeneralizedNotationNotation/src\n2025-06-07 12:29:08,656 - __main__ - DEBUG - Output directory for MCP report: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output\n2025-06-07 12:29:08,656 - __main__ - DEBUG - Verbose: True\n2025-06-07 12:29:08,656 - __main__ - DEBUG - Set logger 'mcp' to DEBUG for MCP operations.\n2025-06-07 12:29:08,656 - __main__ - INFO - \ud83d\ude80 Initializing MCP system to load registered tool descriptions...\n2025-06-07 12:29:08,656 - __main__ - INFO - \u2705 MCP system initialization process completed successfully (SDK status OK, all modules loaded).\n2025-06-07 12:29:08,657 - __main__ - INFO - \ud83d\udd0e Processing MCP integration checks and method discovery...\n2025-06-07 12:29:08,658 - __main__ - DEBUG - \ud83d\udcd6 MCP Core Directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/mcp\n2025-06-07 12:29:08,658 - __main__ - DEBUG - \ud83c\udfd7\ufe0f Project Source Root for modules: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src\n2025-06-07 12:29:08,658 - __main__ - DEBUG - Checking for core MCP files...\n2025-06-07 12:29:08,658 - __main__ - DEBUG - \ud83d\udcd6 Core MCP file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/mcp/mcp.py (20304 bytes)\n2025-06-07 12:29:08,658 - __main__ - DEBUG - \ud83d\udcd6 Core MCP file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/mcp/meta_mcp.py (4954 bytes)\n2025-06-07 12:29:08,658 - __main__ - DEBUG - \ud83d\udcd6 Core MCP file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/mcp/cli.py (4644 bytes)\n2025-06-07 12:29:08,658 - __main__ - DEBUG - \ud83d\udcd6 Core MCP file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/mcp/server_stdio.py (7620 bytes)\n2025-06-07 12:29:08,658 - __main__ - DEBUG - \ud83d\udcd6 Core MCP file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/mcp/server_http.py (7731 bytes)\n2025-06-07 12:29:08,658 - __main__ - DEBUG - Processing module: export\n2025-06-07 12:29:08,658 - __main__ - DEBUG - \u2705 MCP integration file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/export/mcp.py (7976 bytes)\n2025-06-07 12:29:08,658 - __main__ - DEBUG - \ud83d\udc0d Parsing for MCP methods in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/export/mcp.py\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found method: _handle_export(export_func, gnn_file_path, output_file_path, format_name, requires_nx) - \"Generic helper to run an export function and handle common exceptions.\"...\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found method: export_gnn_to_json_mcp(gnn_file_path, output_file_path) - \"\"...\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found method: export_gnn_to_xml_mcp(gnn_file_path, output_file_path) - \"\"...\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found method: export_gnn_to_plaintext_summary_mcp(gnn_file_path, output_file_path) - \"\"...\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found method: export_gnn_to_plaintext_dsl_mcp(gnn_file_path, output_file_path) - \"\"...\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found method: export_gnn_to_gexf_mcp(gnn_file_path, output_file_path) - \"\"...\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found method: export_gnn_to_graphml_mcp(gnn_file_path, output_file_path) - \"\"...\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found method: export_gnn_to_json_adjacency_list_mcp(gnn_file_path, output_file_path) - \"\"...\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found method: export_gnn_to_python_pickle_mcp(gnn_file_path, output_file_path) - \"\"...\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found method: register_tools(mcp_instance) - \"Registers all GNN export tools with the MCP instance.\"...\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): _handle_export(export_func, gnn_file_path, output_file_path, format_name, requires_nx) - Docstring: Generic helper to run an export function and handle common exceptions.\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): export_gnn_to_json_mcp(gnn_file_path, output_file_path) - Docstring: \n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): export_gnn_to_xml_mcp(gnn_file_path, output_file_path) - Docstring: \n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): export_gnn_to_plaintext_summary_mcp(gnn_file_path, output_file_path) - Docstring: \n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): export_gnn_to_plaintext_dsl_mcp(gnn_file_path, output_file_path) - Docstring: \n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): export_gnn_to_gexf_mcp(gnn_file_path, output_file_path) - Docstring: \n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): export_gnn_to_graphml_mcp(gnn_file_path, output_file_path) - Docstring: \n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): export_gnn_to_json_adjacency_list_mcp(gnn_file_path, output_file_path) - Docstring: \n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): export_gnn_to_python_pickle_mcp(gnn_file_path, output_file_path) - Docstring: \n2025-06-07 12:29:08,659 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): register_tools(mcp_instance) - Docstring: Registers all GNN export tools with the MCP instance.\n2025-06-07 12:29:08,659 - __main__ - DEBUG - Processing module: gnn\n2025-06-07 12:29:08,659 - __main__ - DEBUG - \u2705 MCP integration file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/mcp.py (4122 bytes)\n2025-06-07 12:29:08,660 - __main__ - DEBUG - Found registered MCP tool: get_gnn_documentation(doc_name) - Description: Retrieve the content of a GNN core documentation file (e.g., syntax, file structure).\n2025-06-07 12:29:08,660 - __main__ - DEBUG - \ud83d\udc0d Parsing for MCP methods in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/mcp.py\n2025-06-07 12:29:08,660 - __main__ - DEBUG - Found method: get_gnn_documentation(doc_name) - \"Retrieve content of a GNN documentation file.\"...\n2025-06-07 12:29:08,660 - __main__ - DEBUG - Found method: _retrieve_gnn_doc_resource(uri) - \"Retrieve GNN documentation resource by URI.\"...\n2025-06-07 12:29:08,660 - __main__ - DEBUG - Found method: register_tools(mcp_instance) - \"Register GNN documentation tools and resources with the MCP.\"...\n2025-06-07 12:29:08,660 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): _retrieve_gnn_doc_resource(uri) - Docstring: Retrieve GNN documentation resource by URI.\n2025-06-07 12:29:08,660 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): register_tools(mcp_instance) - Docstring: Register GNN documentation tools and resources with the MCP.\n2025-06-07 12:29:08,660 - __main__ - DEBUG - Processing module: gnn_type_checker\n2025-06-07 12:29:08,660 - __main__ - DEBUG - \u2705 MCP integration file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn_type_checker/mcp.py (10921 bytes)\n2025-06-07 12:29:08,660 - __main__ - DEBUG - Found registered MCP tool: type_check_gnn_file(file_path) - Description: Runs the GNN type checker on a specified GNN model file.\n2025-06-07 12:29:08,660 - __main__ - DEBUG - Found registered MCP tool: type_check_gnn_directory(dir_path, recursive, output_dir_base, report_md_filename) - Description: Runs the GNN type checker on all GNN files in a specified directory. If output_dir_base is provided, reports are generated.\n2025-06-07 12:29:08,660 - __main__ - DEBUG - Found registered MCP tool: estimate_resources_for_gnn_file(file_path) - Description: Estimates computational resources (memory, inference, storage) for a GNN model file.\n2025-06-07 12:29:08,660 - __main__ - DEBUG - Found registered MCP tool: estimate_resources_for_gnn_directory(dir_path, recursive) - Description: Estimates computational resources for all GNN files in a specified directory.\n2025-06-07 12:29:08,661 - __main__ - DEBUG - \ud83d\udc0d Parsing for MCP methods in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn_type_checker/mcp.py\n2025-06-07 12:29:08,661 - __main__ - DEBUG - Found method: type_check_gnn_file_mcp(file_path) - \"Run the GNN type checker on a single GNN file. Exposed via MCP.\"...\n2025-06-07 12:29:08,661 - __main__ - DEBUG - Found method: type_check_gnn_directory_mcp(dir_path, recursive, output_dir_base, report_md_filename) - \"Run the GNN type checker on all GNN files in a directory. Exposed via MCP.\"...\n2025-06-07 12:29:08,662 - __main__ - DEBUG - Found method: estimate_resources_for_gnn_file_mcp(file_path) - \"Estimate computational resources for a single GNN file. Exposed via MCP.\"...\n2025-06-07 12:29:08,662 - __main__ - DEBUG - Found method: estimate_resources_for_gnn_directory_mcp(dir_path, recursive) - \"Estimate resources for all GNN files in a directory. Exposed via MCP.\"...\n2025-06-07 12:29:08,662 - __main__ - DEBUG - Found method: register_tools(mcp_instance) - \"Register GNN type checker and resource estimator tools with the MCP.\"...\n2025-06-07 12:29:08,662 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): type_check_gnn_file_mcp(file_path) - Docstring: Run the GNN type checker on a single GNN file. Exposed via MCP.\n2025-06-07 12:29:08,662 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): type_check_gnn_directory_mcp(dir_path, recursive, output_dir_base, report_md_filename) - Docstring: Run the GNN type checker on all GNN files in a directory. Exposed via MCP.\n2025-06-07 12:29:08,662 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): estimate_resources_for_gnn_file_mcp(file_path) - Docstring: Estimate computational resources for a single GNN file. Exposed via MCP.\n2025-06-07 12:29:08,662 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): estimate_resources_for_gnn_directory_mcp(dir_path, recursive) - Docstring: Estimate resources for all GNN files in a directory. Exposed via MCP.\n2025-06-07 12:29:08,662 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): register_tools(mcp_instance) - Docstring: Register GNN type checker and resource estimator tools with the MCP.\n2025-06-07 12:29:08,662 - __main__ - DEBUG - Processing module: ontology\n2025-06-07 12:29:08,662 - __main__ - DEBUG - \u2705 MCP integration file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/ontology/mcp.py (13473 bytes)\n2025-06-07 12:29:08,662 - __main__ - DEBUG - \ud83d\udc0d Parsing for MCP methods in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/ontology/mcp.py\n2025-06-07 12:29:08,663 - __main__ - DEBUG - Found method: get_mcp_interface() - \"Returns the MCP interface for the Ontology module.\"...\n2025-06-07 12:29:08,663 - __main__ - DEBUG - Found method: parse_gnn_ontology_section(gnn_file_content, verbose) - \"Parses the 'ActInfOntologyAnnotation' section from GNN file content.\"...\n2025-06-07 12:29:08,663 - __main__ - DEBUG - Found method: load_defined_ontology_terms(ontology_terms_path, verbose) - \"Loads defined ontological terms from a JSON file.\"...\n2025-06-07 12:29:08,663 - __main__ - DEBUG - Found method: validate_annotations(parsed_annotations, defined_terms, verbose) - \"Validates parsed GNN annotations against a set of defined ontological terms.\"...\n2025-06-07 12:29:08,663 - __main__ - DEBUG - Found method: generate_ontology_report_for_file(gnn_file_path, parsed_annotations, validation_results) - \"Generates a markdown formatted report string for a single GNN file's ontology annotations.\"...\n2025-06-07 12:29:08,663 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): get_mcp_interface() - Docstring: Returns the MCP interface for the Ontology module.\n2025-06-07 12:29:08,663 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): parse_gnn_ontology_section(gnn_file_content, verbose) - Docstring: Parses the 'ActInfOntologyAnnotation' section from GNN file content.\n2025-06-07 12:29:08,663 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): load_defined_ontology_terms(ontology_terms_path, verbose) - Docstring: Loads defined ontological terms from a JSON file.\n2025-06-07 12:29:08,663 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): validate_annotations(parsed_annotations, defined_terms, verbose) - Docstring: Validates parsed GNN annotations against a set of defined ontological terms.\n2025-06-07 12:29:08,663 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): generate_ontology_report_for_file(gnn_file_path, parsed_annotations, validation_results) - Docstring: Generates a markdown formatted report string for a single GNN file's ontology annotations.\n2025-06-07 12:29:08,663 - __main__ - DEBUG - Processing module: setup\n2025-06-07 12:29:08,664 - __main__ - DEBUG - \u2705 MCP integration file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/setup/mcp.py (4257 bytes)\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found registered MCP tool: ensure_directory_exists(directory_path) - Description: Ensures a directory exists, creating it if necessary. Returns the absolute path.\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found registered MCP tool: find_project_gnn_files(search_directory, recursive) - Description: Finds all GNN (.md) files in a specified directory within the project.\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found registered MCP tool: get_standard_output_paths(base_output_directory) - Description: Gets a dictionary of standard output directory paths (e.g., for type_check, visualization), creating them if needed.\n2025-06-07 12:29:08,664 - __main__ - DEBUG - \ud83d\udc0d Parsing for MCP methods in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/setup/mcp.py\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found method: ensure_directory_exists_mcp(directory_path) - \"Ensure a directory exists, creating it if necessary. Exposed via MCP.\"...\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found method: find_project_gnn_files_mcp(search_directory, recursive) - \"Find all GNN (.md) files in a directory. Exposed via MCP.\"...\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found method: get_standard_output_paths_mcp(base_output_directory) - \"Get standard output paths for the pipeline. Exposed via MCP.\"...\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found method: register_tools(mcp_instance) - \"Register setup utility tools with the MCP.\"...\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): ensure_directory_exists_mcp(directory_path) - Docstring: Ensure a directory exists, creating it if necessary. Exposed via MCP.\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): find_project_gnn_files_mcp(search_directory, recursive) - Docstring: Find all GNN (.md) files in a directory. Exposed via MCP.\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): get_standard_output_paths_mcp(base_output_directory) - Docstring: Get standard output paths for the pipeline. Exposed via MCP.\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): register_tools(mcp_instance) - Docstring: Register setup utility tools with the MCP.\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Processing module: tests\n2025-06-07 12:29:08,664 - __main__ - DEBUG - \u2705 MCP integration file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/tests/mcp.py (7083 bytes)\n2025-06-07 12:29:08,664 - __main__ - DEBUG - Found registered MCP tool: run_gnn_type_checker(file_path) - Description: Run the GNN type checker on a specific file (via test module).\n2025-06-07 12:29:08,665 - __main__ - DEBUG - Found registered MCP tool: run_gnn_type_checker_on_directory(dir_path, report_file) - Description: Run the GNN type checker on all GNN files in a directory (via test module).\n2025-06-07 12:29:08,665 - __main__ - DEBUG - Found registered MCP tool: run_gnn_unit_tests() - Description: Run the GNN unit tests and return results.\n2025-06-07 12:29:08,665 - __main__ - DEBUG - \ud83d\udc0d Parsing for MCP methods in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/tests/mcp.py\n2025-06-07 12:29:08,665 - __main__ - DEBUG - Found method: run_type_checker_on_file(file_path) - \"Run the GNN type checker on a file.\"...\n2025-06-07 12:29:08,665 - __main__ - DEBUG - Found method: run_type_checker_on_directory(dir_path, report_file) - \"Run the GNN type checker on a directory of files.\"...\n2025-06-07 12:29:08,665 - __main__ - DEBUG - Found method: run_unit_tests() - \"Run the GNN unit tests.\"...\n2025-06-07 12:29:08,665 - __main__ - DEBUG - Found method: get_test_report(uri) - \"Retrieve a test report by URI.\"...\n2025-06-07 12:29:08,665 - __main__ - DEBUG - Found method: register_tools(mcp) - \"Register test tools with the MCP.\"...\n2025-06-07 12:29:08,666 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): run_type_checker_on_file(file_path) - Docstring: Run the GNN type checker on a file.\n2025-06-07 12:29:08,666 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): run_type_checker_on_directory(dir_path, report_file) - Docstring: Run the GNN type checker on a directory of files.\n2025-06-07 12:29:08,666 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): run_unit_tests() - Docstring: Run the GNN unit tests.\n2025-06-07 12:29:08,666 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): get_test_report(uri) - Docstring: Retrieve a test report by URI.\n2025-06-07 12:29:08,666 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): register_tools(mcp) - Docstring: Register test tools with the MCP.\n2025-06-07 12:29:08,666 - __main__ - DEBUG - Processing module: visualization\n2025-06-07 12:29:08,666 - __main__ - DEBUG - \u2705 MCP integration file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/visualization/mcp.py (5934 bytes)\n2025-06-07 12:29:08,666 - __main__ - DEBUG - Found registered MCP tool: visualize_gnn_file(file_path, output_dir) - Description: Generate visualizations for a specific GNN file.\n2025-06-07 12:29:08,666 - __main__ - DEBUG - Found registered MCP tool: visualize_gnn_directory(dir_path, output_dir) - Description: Visualize all GNN files in a directory\n2025-06-07 12:29:08,666 - __main__ - DEBUG - Found registered MCP tool: parse_gnn_file(file_path) - Description: Parse a GNN file without visualization\n2025-06-07 12:29:08,666 - __main__ - DEBUG - \ud83d\udc0d Parsing for MCP methods in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/visualization/mcp.py\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found method: visualize_file(file_path, output_dir) - \"Visualize a GNN file through MCP.\"...\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found method: visualize_directory(dir_path, output_dir) - \"Visualize all GNN files in a directory through MCP.\"...\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found method: parse_gnn_file(file_path) - \"Parse a GNN file without visualization through MCP.\"...\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found method: get_visualization_results(uri) - \"Retrieve visualization results by URI.\"...\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found method: register_tools(mcp) - \"Register visualization tools with the MCP.\"...\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): visualize_file(file_path, output_dir) - Docstring: Visualize a GNN file through MCP.\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): visualize_directory(dir_path, output_dir) - Docstring: Visualize all GNN files in a directory through MCP.\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): get_visualization_results(uri) - Docstring: Retrieve visualization results by URI.\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): register_tools(mcp) - Docstring: Register visualization tools with the MCP.\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Processing module: llm\n2025-06-07 12:29:08,667 - __main__ - DEBUG - \u2705 MCP integration file found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/llm/mcp.py (19238 bytes)\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found registered MCP tool: llm.summarize_gnn_file(file_path_str, user_prompt_suffix) - Description: Reads a GNN specification file and uses an LLM to generate a concise summary of its content. Optionally, a user prompt suffix can refine the summary focus.\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found registered MCP tool: llm.explain_gnn_file(file_path_str, aspect_to_explain) - Description: Reads a GNN specification file and uses an LLM to generate an explanation of its content. Can focus on a specific aspect if provided.\n2025-06-07 12:29:08,667 - __main__ - DEBUG - Found registered MCP tool: llm.generate_professional_summary(file_path_str, experiment_details, target_audience) - Description: Reads a GNN file and optional experiment details, then uses an LLM to generate a professional summary suitable for reports or papers.\n2025-06-07 12:29:08,667 - __main__ - DEBUG - \ud83d\udc0d Parsing for MCP methods in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/llm/mcp.py\n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found method: initialize_llm_module(mcp_instance_ref) - \"Initializes the LLM module, loads API key, and updates MCP status.\"...\n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found method: summarize_gnn_file_content(file_path_str, user_prompt_suffix) - \"Reads a GNN file, sends its content to an LLM, and returns a summary.\"...\n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found method: explain_gnn_file_content(file_path_str, aspect_to_explain) - \"Reads a GNN file, sends its content to an LLM, and returns an explanation.\"...\n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found method: generate_professional_summary_from_gnn(file_path_str, experiment_details, target_audience) - \"Generates a professional summary of a GNN model and its experimental context.\"...\n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found method: register_tools(mcp_instance_ref) - \"\"...\n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found method: ensure_llm_tools_registered(mcp_instance_ref) - \"Ensures that LLM tools are registered with the provided MCP instance.\"...\n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): initialize_llm_module(mcp_instance_ref) - Docstring: Initializes the LLM module, loads API key, and updates MCP status.\n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): summarize_gnn_file_content(file_path_str, user_prompt_suffix) - Docstring: Reads a GNN file, sends its content to an LLM, and returns a summary.\n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): explain_gnn_file_content(file_path_str, aspect_to_explain) - Docstring: Reads a GNN file, sends its content to an LLM, and returns an explanation.\n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): generate_professional_summary_from_gnn(file_path_str, experiment_details, target_audience) - Docstring: Generates a professional summary of a GNN model and its experimental context.\n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): register_tools(mcp_instance_ref) - Docstring: \n2025-06-07 12:29:08,669 - __main__ - DEBUG - Found AST method (not a direct MCP tool or already listed): ensure_llm_tools_registered(mcp_instance_ref) - Docstring: Ensures that LLM tools are registered with the provided MCP instance.\n2025-06-07 12:29:08,669 - __main__ - DEBUG - \u2705 MCP integration and API report saved: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/mcp_processing_step/7_mcp_integration_report.md (34643 bytes)\n2025-06-07 12:29:08,670 - __main__ - INFO - \u2705 Step 7: MCP Operations (7_mcp.py) - COMPLETED (Report generated; check report for details on findings)\n",
"stderr": "Failed to load MCP module src.export.mcp: No module named 'src.export.structured_data_exporters'\nFailed to load MCP module src.export.mcp: No module named 'src.export.structured_data_exporters'\n2025-06-07 12:29:08,399 - root - WARNING - Could not directly import MCPTool from src.mcp. This might be okay if loaded by the main MCP system.\n2025-06-07 12:29:08,409 - src.render.mcp - INFO - Render module MCP tools registered.\n2025-06-07 12:29:08,652 - src.llm.mcp - INFO - Successfully imported MCPTool and MCPSDKNotFoundError from src.mcp.mcp in initialize_llm_module.\n2025-06-07 12:29:08,653 - llm.llm_operations - INFO - OpenAI API key loaded successfully.\n2025-06-07 12:29:08,653 - src.llm.mcp - INFO - LLM API Key loaded successfully.\n2025-06-07 12:29:08,653 - src.llm.mcp - INFO - Registered MCP tool: llm.summarize_gnn_file\n2025-06-07 12:29:08,653 - src.llm.mcp - INFO - Registered MCP tool: llm.explain_gnn_file\n2025-06-07 12:29:08,653 - src.llm.mcp - INFO - Registered MCP tool: llm.generate_professional_summary\n2025-06-07 12:29:08,653 - src.llm.mcp - INFO - LLM module MCP tools registration process completed.\n2025-06-07 12:29:08,653 - src.mcp.sympy_mcp - INFO - Successfully registered SymPy MCP tools\n2025-06-07 12:29:08,653 - mcp - INFO - Successfully registered SymPy MCP tools\n2025-06-07 12:29:08,653 - mcp - INFO - MCP system initialized using project's internal MCP components. SDK Status: Using project's internal MCP implementation.\n2025-06-07 12:29:08,653 - src.visualization.mcp - INFO - Visualization module MCP tools and resources registered.\n2025-06-07 12:29:08,654 - src.render.mcp - INFO - Render module MCP tools registered.\n2025-06-07 12:29:08,654 - src.llm.mcp - INFO - Successfully imported MCPTool and MCPSDKNotFoundError from src.mcp.mcp in initialize_llm_module.\n2025-06-07 12:29:08,654 - llm.llm_operations - INFO - OpenAI API key loaded successfully.\n2025-06-07 12:29:08,654 - src.llm.mcp - INFO - LLM API Key loaded successfully.\n2025-06-07 12:29:08,654 - src.llm.mcp - INFO - Registered MCP tool: llm.summarize_gnn_file\n2025-06-07 12:29:08,654 - src.llm.mcp - INFO - Registered MCP tool: llm.explain_gnn_file\n2025-06-07 12:29:08,654 - src.llm.mcp - INFO - Registered MCP tool: llm.generate_professional_summary\n2025-06-07 12:29:08,654 - src.llm.mcp - INFO - LLM module MCP tools registration process completed.\n2025-06-07 12:29:08,655 - src.mcp.sympy_mcp - INFO - Successfully registered SymPy MCP tools\n2025-06-07 12:29:08,655 - mcp - INFO - Successfully registered SymPy MCP tools\n2025-06-07 12:29:08,655 - mcp - INFO - MCP system initialized using project's internal MCP components. SDK Status: Using project's internal MCP implementation.\n2025-06-07 12:29:08,656 - mcp - DEBUG - MCP modules already discovered. Skipping redundant discovery.\n2025-06-07 12:29:08,656 - mcp - INFO - MCP system initialized using project's internal MCP components. SDK Status: Using project's internal MCP implementation.\n"
},
{
"step_number": 8,
"script_name": "8_ontology.py",
"status": "SUCCESS",
"start_time": "2025-06-07T12:29:08.962291",
"end_time": "2025-06-07T12:29:09.014244",
"duration_seconds": 0.051953,
"details": "",
"stdout": "2025-06-07 12:29:09,004 - __main__ - INFO - \u25b6\ufe0f Starting Step 8: Ontology Operations (8_ontology.py)\n2025-06-07 12:29:09,004 - __main__ - DEBUG - Parsed options (from main.py or standalone):\n2025-06-07 12:29:09,004 - __main__ - DEBUG - Target GNN files directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples\n2025-06-07 12:29:09,004 - __main__ - DEBUG - Output directory for report: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output\n2025-06-07 12:29:09,004 - __main__ - DEBUG - Recursive: True\n2025-06-07 12:29:09,004 - __main__ - DEBUG - Ontology terms definition file: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/ontology/act_inf_ontology_terms.json\n2025-06-07 12:29:09,004 - __main__ - DEBUG - Verbose flag from args: True\n2025-06-07 12:29:09,004 - __main__ - INFO - \ud83d\udd0e Processing ontology related tasks...\n2025-06-07 12:29:09,004 - __main__ - DEBUG - \ud83c\udfaf Target GNN files in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples\n2025-06-07 12:29:09,004 - __main__ - DEBUG - \u0565\u056c Output directory for ontology report: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output\n2025-06-07 12:29:09,004 - __main__ - DEBUG - \ud83d\udd04 Recursive mode: Enabled\n2025-06-07 12:29:09,004 - __main__ - DEBUG - \ud83d\udcd6 Using ontology terms definition from: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/ontology/act_inf_ontology_terms.json\n2025-06-07 12:29:09,004 - __main__ - DEBUG - \ud83e\udde0 Conceptual Note: Ontologies provide a formal way to represent knowledge.\n2025-06-07 12:29:09,004 - __main__ - DEBUG - - Informal ontologies (like folksonomies or taxonomies) help organize concepts.\n2025-06-07 12:29:09,004 - __main__ - DEBUG - - Formal ontologies (e.g., in OWL, RDF) allow for logical reasoning and consistency checks.\n2025-06-07 12:29:09,004 - __main__ - DEBUG - - This script focuses on extracting and validating terms based on a predefined JSON schema.\n2025-06-07 12:29:09,004 - __main__ - DEBUG - - Different ontology languages (OWL, RDF, SKOS) offer varying expressiveness.\n2025-06-07 12:29:09,004 - __main__ - DEBUG - \u270d\ufe0f Ontology report will be saved in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/ontology_processing\n2025-06-07 12:29:09,005 - __main__ - DEBUG - \ud83e\uddd0 Loading defined ontology terms from: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/ontology/act_inf_ontology_terms.json\n2025-06-07 12:29:09,005 - __main__ - DEBUG - \ud83d\udcda Loaded 48 ontology terms successfully.\n2025-06-07 12:29:09,005 - __main__ - DEBUG - \ud83d\udcca Found 2 GNN (.md) files to process.\n2025-06-07 12:29:09,005 - __main__ - DEBUG - \ud83d\udcc4 Processing file: pymdp_pomdp_agent.md (6387 bytes)\n2025-06-07 12:29:09,006 - __main__ - DEBUG - Found 20 ontology annotations in pymdp_pomdp_agent.md.\n2025-06-07 12:29:09,006 - __main__ - DEBUG - Validated for pymdp_pomdp_agent.md: 20 passed, 0 failed.\n2025-06-07 12:29:09,006 - __main__ - DEBUG - \ud83d\udcc4 Processing file: rxinfer_multiagent_gnn.md (9002 bytes)\n2025-06-07 12:29:09,006 - __main__ - DEBUG - Found 12 ontology annotations in rxinfer_multiagent_gnn.md.\n2025-06-07 12:29:09,006 - __main__ - DEBUG - Validated for rxinfer_multiagent_gnn.md: 0 passed, 12 failed.\n2025-06-07 12:29:09,006 - __main__ - DEBUG - \u2705 Ontology processing report saved: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/ontology_processing/ontology_processing_report.md (2204 bytes)\n2025-06-07 12:29:09,006 - __main__ - WARNING - \u26a0\ufe0f Step 8: Ontology validation completed with 12 failed term(s). Check '/home/trim/Documents/GitHub/GeneralizedNotationNotation/output/ontology_processing/ontology_processing_report.md' for details.\n",
"stderr": ""
},
{
"step_number": 9,
"script_name": "9_render.py",
"status": "SUCCESS",
"start_time": "2025-06-07T12:29:09.014283",
"end_time": "2025-06-07T12:29:10.190708",
"duration_seconds": 1.176425,
"details": "",
"stdout": "",
"stderr": "2025-06-07 12:29:10,061 - __main__ - INFO - Found 2 GNN JSON files to render.\n2025-06-07 12:29:10,061 - __main__ - INFO - Processing pymdp_pomdp_agent.json\n2025-06-07 12:29:10,061 - render.pymdp_renderer - INFO - Initializing GNN to PyMDP converter for model: UnknownModel\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - Starting GNN data extraction.\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - StateSpaceBlock not found or empty in GNN spec.\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - ModelParameters not found or empty in GNN spec.\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - Finished GNN data extraction.\n2025-06-07 12:29:10,061 - render.pymdp_renderer - INFO - Generating PyMDP Python script content...\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - Matrix definitions not generated yet. Generating matrices...\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - A_matrix: No observation modalities defined. 'A' will be None.\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - B_matrix: No hidden state factors defined. 'B' will be None.\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - C_vector: No observation modalities defined. 'C' will be None.\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - D_vector: No hidden state factors defined. 'D' will be None.\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - E_vector: No E (policy prior) specification found. Defaulting to None.\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - Agent instantiation code not generated yet. Generating...\n2025-06-07 12:29:10,061 - render.pymdp_converter - ERROR - Cannot generate agent instantiation with no observation modalities or hidden state factors.\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - Example usage code not generated yet. Generating...\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - Skipping example usage code due to missing modalities or state factors.\n2025-06-07 12:29:10,061 - render.pymdp_converter - INFO - Generated full Python script (4802 characters)\n2025-06-07 12:29:10,061 - render.pymdp_renderer - INFO - Writing PyMDP script to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_rendered_simulators/pymdp/pymdp_pomdp_agent_pymdp.py\n2025-06-07 12:29:10,061 - render.pymdp_renderer - INFO - Successfully wrote PyMDP script: pymdp_pomdp_agent_pymdp.py\n2025-06-07 12:29:10,061 - __main__ - INFO - Successfully rendered to PyMDP: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_rendered_simulators/pymdp/pymdp_pomdp_agent_pymdp.py\n2025-06-07 12:29:10,061 - render.rxinfer.toml_generator - INFO - Generating RxInfer TOML configuration at /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_rendered_simulators/rxinfer_toml/pymdp_pomdp_agent_config.toml\n2025-06-07 12:29:10,062 - render.rxinfer.toml_generator - INFO - Successfully wrote TOML configuration to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_rendered_simulators/rxinfer_toml/pymdp_pomdp_agent_config.toml\n2025-06-07 12:29:10,062 - __main__ - INFO - Successfully rendered to RxInfer TOML: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_rendered_simulators/rxinfer_toml/pymdp_pomdp_agent_config.toml\n2025-06-07 12:29:10,062 - __main__ - INFO - Processing rxinfer_multiagent_gnn.json\n2025-06-07 12:29:10,062 - render.pymdp_renderer - INFO - Initializing GNN to PyMDP converter for model: UnknownModel\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - Starting GNN data extraction.\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - StateSpaceBlock not found or empty in GNN spec.\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - ModelParameters not found or empty in GNN spec.\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - Finished GNN data extraction.\n2025-06-07 12:29:10,062 - render.pymdp_renderer - INFO - Generating PyMDP Python script content...\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - Matrix definitions not generated yet. Generating matrices...\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - A_matrix: No observation modalities defined. 'A' will be None.\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - B_matrix: No hidden state factors defined. 'B' will be None.\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - C_vector: No observation modalities defined. 'C' will be None.\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - D_vector: No hidden state factors defined. 'D' will be None.\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - E_vector: No E (policy prior) specification found. Defaulting to None.\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - Agent instantiation code not generated yet. Generating...\n2025-06-07 12:29:10,062 - render.pymdp_converter - ERROR - Cannot generate agent instantiation with no observation modalities or hidden state factors.\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - Example usage code not generated yet. Generating...\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - Skipping example usage code due to missing modalities or state factors.\n2025-06-07 12:29:10,062 - render.pymdp_converter - INFO - Generated full Python script (4807 characters)\n2025-06-07 12:29:10,062 - render.pymdp_renderer - INFO - Writing PyMDP script to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_rendered_simulators/pymdp/rxinfer_multiagent_gnn_pymdp.py\n2025-06-07 12:29:10,062 - render.pymdp_renderer - INFO - Successfully wrote PyMDP script: rxinfer_multiagent_gnn_pymdp.py\n2025-06-07 12:29:10,062 - __main__ - INFO - Successfully rendered to PyMDP: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_rendered_simulators/pymdp/rxinfer_multiagent_gnn_pymdp.py\n2025-06-07 12:29:10,062 - render.rxinfer.toml_generator - INFO - Generating RxInfer TOML configuration at /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_rendered_simulators/rxinfer_toml/rxinfer_multiagent_gnn_config.toml\n2025-06-07 12:29:10,062 - render.rxinfer.toml_generator - INFO - Successfully wrote TOML configuration to /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_rendered_simulators/rxinfer_toml/rxinfer_multiagent_gnn_config.toml\n2025-06-07 12:29:10,063 - __main__ - INFO - Successfully rendered to RxInfer TOML: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_rendered_simulators/rxinfer_toml/rxinfer_multiagent_gnn_config.toml\n"
},
{
"step_number": 10,
"script_name": "10_execute.py",
"status": "SUCCESS",
"start_time": "2025-06-07T12:29:10.190749",
"end_time": "2025-06-07T12:29:12.566250",
"duration_seconds": 2.375501,
"details": "",
"stdout": "2025-06-07 12:29:10,231 - __main__ - DEBUG - Verbose logging extended to 'execute.pymdp_runner' module.\n2025-06-07 12:29:10,231 - __main__ - DEBUG - Verbose logging extended to 'execute.rxinfer_runner' module.\n2025-06-07 12:29:10,231 - __main__ - INFO - \u25b6\ufe0f Starting Step 10: Execute Rendered Simulators (10_execute.py)\n2025-06-07 12:29:10,232 - __main__ - DEBUG - Pipeline arguments received: Namespace(output_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/output'), recursive=True, verbose=True, pymdp_only=False, rxinfer_only=False, target_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples'))\n2025-06-07 12:29:10,232 - __main__ - INFO - Executing PyMDP rendered scripts...\n2025-06-07 12:29:12,547 - __main__ - INFO - \u2705 PyMDP scripts executed successfully or no scripts were found to run.\n2025-06-07 12:29:12,547 - __main__ - INFO - Executing RxInfer.jl rendered scripts...\n2025-06-07 12:29:12,559 - __main__ - INFO - \u2705 RxInfer.jl scripts executed successfully or no scripts were found to run.\n2025-06-07 12:29:12,559 - __main__ - INFO - \u2705 Step 10: Execute Rendered Simulators (10_execute.py) - COMPLETED\n",
"stderr": "Directory not found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_rendered_simulators/rxinfer\n"
},
{
"step_number": 11,
"script_name": "11_llm.py",
"status": "FAILED_TIMEOUT",
"start_time": "2025-06-07T12:29:12.566288",
"end_time": "2025-06-07T12:31:12.611599",
"duration_seconds": 120.045311,
"details": "Process timed out after 120.0 seconds (limit: 120s)",
"stdout": "2025-06-07 12:29:13,979 - __main__ - INFO - Standalone run: Ensuring LLM tools are registered (if not already done)...\n2025-06-07 12:29:13,979 - __main__ - INFO - Ensuring LLM tools are registered (one-time setup)...\n2025-06-07 12:29:13,980 - __main__ - INFO - LLM tools registration check complete and API key loaded.\n2025-06-07 12:29:13,980 - __main__ - INFO - Standalone run: LLM tools registration check complete.\n2025-06-07 12:29:13,980 - __main__ - DEBUG - Script logger '__main__' level set to DEBUG.\n2025-06-07 12:29:13,980 - __main__ - DEBUG - Verbose mode: Set logger for 'src.llm.llm_operations' to DEBUG.\n2025-06-07 12:29:13,980 - __main__ - INFO - Starting LLM processing step. Target directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples, Output directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output\n2025-06-07 12:29:13,980 - __main__ - DEBUG - Full arguments: Namespace(target_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples'), output_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/output'), recursive=True, verbose=True, llm_tasks=['all'], llm_timeout=60)\n2025-06-07 12:29:13,980 - __main__ - INFO - LLM outputs will be saved in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step\n2025-06-07 12:29:13,980 - __main__ - INFO - LLM tasks to run: summary, analysis, qa\n2025-06-07 12:29:13,981 - __main__ - INFO - Discovered 2 potential GNN input files.\n2025-06-07 12:29:13,981 - __main__ - DEBUG - - Found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/pymdp_pomdp_agent.md\n2025-06-07 12:29:13,981 - __main__ - DEBUG - - Found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md\n2025-06-07 12:29:13,981 - __main__ - INFO - --- Processing GNN file: pymdp_pomdp_agent.md ---\n2025-06-07 12:29:13,981 - __main__ - INFO - Processing GNN file with LLM: pymdp_pomdp_agent.md\n2025-06-07 12:29:13,981 - __main__ - INFO - Task 1: Generating summary for pymdp_pomdp_agent.md...\n2025-06-07 12:29:13,981 - __main__ - DEBUG - Summary prompt for pymdp_pomdp_agent.md (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:20,739 - __main__ - INFO - Successfully generated summary for pymdp_pomdp_agent.md. Saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step/pymdp_pomdp_agent/pymdp_pomdp_agent_summary.txt (Size: 1610 bytes)\n2025-06-07 12:29:20,740 - __main__ - INFO - Task 2: Generating comprehensive analysis for pymdp_pomdp_agent.md...\n2025-06-07 12:29:20,740 - __main__ - DEBUG - Comprehensive analysis prompt for pymdp_pomdp_agent.md (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:42,239 - __main__ - INFO - Successfully generated comprehensive analysis for pymdp_pomdp_agent.md. Saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step/pymdp_pomdp_agent/pymdp_pomdp_agent_comprehensive_analysis.json (Size: 5285 bytes)\n2025-06-07 12:29:42,239 - __main__ - INFO - Tasks 3 & 4: Generating Q&A for pymdp_pomdp_agent.md...\n2025-06-07 12:29:42,239 - __main__ - DEBUG - Question generation prompt for pymdp_pomdp_agent.md (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:45,341 - __main__ - INFO - Successfully generated 5 questions for pymdp_pomdp_agent.md.\n2025-06-07 12:29:45,341 - __main__ - INFO - Answering 5 generated questions for pymdp_pomdp_agent.md...\n2025-06-07 12:29:45,341 - __main__ - DEBUG - Answering question 1/5: 'What are the implications of having multiple observation modalities on the decision-making process of the Multifactor PyMDP Agent?'\n2025-06-07 12:29:49,787 - __main__ - DEBUG - Answer for 'What are the implications of having multiple observation modalities on the decision-making process of the Multifactor PyMDP Agent?': The GNN file indicates that the Multifactor PyMDP Agent utilizes multiple observation modalities\u2014spe...\n2025-06-07 12:29:49,787 - __main__ - DEBUG - Answering question 2/5: 'How does the control over the 'decision_state' factor influence the overall performance and adaptability of the agent in varying environments?'\n2025-06-07 12:29:51,339 - __main__ - DEBUG - Answer for 'How does the control over the 'decision_state' factor influence the overall performance and adaptability of the agent in varying environments?': The GNN file does not provide sufficient information to directly answer how control over the 'decisi...\n2025-06-07 12:29:51,339 - __main__ - DEBUG - Answering question 3/5: 'In what ways do the defined likelihood matrices (A_m) and transition matrices (B_f) affect the agent's ability to infer states and select actions?'\n2025-06-07 12:29:56,949 - __main__ - DEBUG - Answer for 'In what ways do the defined likelihood matrices (A_m) and transition matrices (B_f) affect the agent's ability to infer states and select actions?': The defined likelihood matrices (A_m) and transition matrices (B_f) play crucial roles in the agent'...\n2025-06-07 12:29:56,949 - __main__ - DEBUG - Answering question 4/5: 'What assumptions are made regarding the independence of hidden state factors in this model, and how might these assumptions impact the agent's learning and decision-making?'\n2025-06-07 12:30:00,432 - __main__ - DEBUG - Answer for 'What assumptions are made regarding the independence of hidden state factors in this model, and how might these assumptions impact the agent's learning and decision-making?': The GNN file content does not explicitly state any assumptions regarding the independence of hidden ...\n2025-06-07 12:30:00,432 - __main__ - DEBUG - Answering question 5/5: 'How does the structure of the initial parameterization affect the convergence and stability of the agent's learning process during simulations?'\n2025-06-07 12:30:02,406 - __main__ - DEBUG - Answer for 'How does the structure of the initial parameterization affect the convergence and stability of the agent's learning process during simulations?': The GNN file does not contain sufficient information to directly assess how the structure of the ini...\n2025-06-07 12:30:02,407 - __main__ - INFO - Successfully generated and answered questions for pymdp_pomdp_agent.md. Saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step/pymdp_pomdp_agent/pymdp_pomdp_agent_qa.json (Size: 5758 bytes)\n2025-06-07 12:30:02,407 - __main__ - INFO - Finished LLM processing for GNN file: pymdp_pomdp_agent.md\n2025-06-07 12:30:02,407 - __main__ - INFO - --- Processing GNN file: rxinfer_multiagent_gnn.md ---\n2025-06-07 12:30:02,407 - __main__ - INFO - Processing GNN file with LLM: rxinfer_multiagent_gnn.md\n2025-06-07 12:30:02,407 - __main__ - INFO - Task 1: Generating summary for rxinfer_multiagent_gnn.md...\n2025-06-07 12:30:02,407 - __main__ - DEBUG - Summary prompt for rxinfer_multiagent_gnn.md (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:09,275 - __main__ - INFO - Successfully generated summary for rxinfer_multiagent_gnn.md. Saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step/rxinfer_multiagent_gnn/rxinfer_multiagent_gnn_summary.txt (Size: 1921 bytes)\n2025-06-07 12:30:09,275 - __main__ - INFO - Task 2: Generating comprehensive analysis for rxinfer_multiagent_gnn.md...\n2025-06-07 12:30:09,275 - __main__ - DEBUG - Comprehensive analysis prompt for rxinfer_multiagent_gnn.md (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:59,215 - __main__ - INFO - Successfully generated comprehensive analysis for rxinfer_multiagent_gnn.md. Saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step/rxinfer_multiagent_gnn/rxinfer_multiagent_gnn_comprehensive_analysis.json (Size: 5355 bytes)\n2025-06-07 12:30:59,215 - __main__ - INFO - Tasks 3 & 4: Generating Q&A for rxinfer_multiagent_gnn.md...\n2025-06-07 12:30:59,215 - __main__ - DEBUG - Question generation prompt for rxinfer_multiagent_gnn.md (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:01,666 - __main__ - INFO - Successfully generated 5 questions for rxinfer_multiagent_gnn.md.\n2025-06-07 12:31:01,666 - __main__ - INFO - Answering 5 generated questions for rxinfer_multiagent_gnn.md...\n2025-06-07 12:31:01,666 - __main__ - DEBUG - Answering question 1/5: 'How does the model handle the trade-off between goal-directed behavior and obstacle avoidance in multi-agent trajectory planning?'\n2025-06-07 12:31:05,347 - __main__ - DEBUG - Answer for 'How does the model handle the trade-off between goal-directed behavior and obstacle avoidance in multi-agent trajectory planning?': The model handles the trade-off between goal-directed behavior and obstacle avoidance in multi-agent...\n2025-06-07 12:31:05,347 - __main__ - DEBUG - Answering question 2/5: 'What is the significance of the softmin_temperature parameter, and how does it influence the agents' decision-making process?'\n2025-06-07 12:31:06,687 - __main__ - DEBUG - Answer for 'What is the significance of the softmin_temperature parameter, and how does it influence the agents' decision-making process?': The GNN file does not provide specific information about the significance of the softmin_temperature...\n2025-06-07 12:31:06,687 - __main__ - DEBUG - Answering question 3/5: 'In what ways do the specified variances (initial_state_variance, control_variance, and goal_constraint_variance) impact the performance of the trajectory planning model?'\n2025-06-07 12:31:11,294 - __main__ - DEBUG - Answer for 'In what ways do the specified variances (initial_state_variance, control_variance, and goal_constraint_variance) impact the performance of the trajectory planning model?': The GNN file specifies the following variances that impact the performance of the trajectory plannin...\n2025-06-07 12:31:11,294 - __main__ - DEBUG - Answering question 4/5: 'How are the collision avoidance constraints structured in the model, and what assumptions are made about agent interactions?'\n2025-06-07 12:29:13,979 - __main__ - INFO - Standalone run: Ensuring LLM tools are registered (if not already done)...\n2025-06-07 12:29:13,979 - __main__ - INFO - Ensuring LLM tools are registered (one-time setup)...\n2025-06-07 12:29:13,980 - __main__ - INFO - LLM tools registration check complete and API key loaded.\n2025-06-07 12:29:13,980 - __main__ - INFO - Standalone run: LLM tools registration check complete.\n2025-06-07 12:29:13,980 - __main__ - DEBUG - Script logger '__main__' level set to DEBUG.\n2025-06-07 12:29:13,980 - __main__ - DEBUG - Verbose mode: Set logger for 'src.llm.llm_operations' to DEBUG.\n2025-06-07 12:29:13,980 - __main__ - INFO - Starting LLM processing step. Target directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples, Output directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output\n2025-06-07 12:29:13,980 - __main__ - DEBUG - Full arguments: Namespace(target_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples'), output_dir=PosixPath('/home/trim/Documents/GitHub/GeneralizedNotationNotation/output'), recursive=True, verbose=True, llm_tasks=['all'], llm_timeout=60)\n2025-06-07 12:29:13,980 - __main__ - INFO - LLM outputs will be saved in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step\n2025-06-07 12:29:13,980 - __main__ - INFO - LLM tasks to run: summary, analysis, qa\n2025-06-07 12:29:13,981 - __main__ - INFO - Discovered 2 potential GNN input files.\n2025-06-07 12:29:13,981 - __main__ - DEBUG - - Found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/pymdp_pomdp_agent.md\n2025-06-07 12:29:13,981 - __main__ - DEBUG - - Found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md\n2025-06-07 12:29:13,981 - __main__ - INFO - --- Processing GNN file: pymdp_pomdp_agent.md ---\n2025-06-07 12:29:13,981 - __main__ - INFO - Processing GNN file with LLM: pymdp_pomdp_agent.md\n2025-06-07 12:29:13,981 - __main__ - INFO - Task 1: Generating summary for pymdp_pomdp_agent.md...\n2025-06-07 12:29:13,981 - __main__ - DEBUG - Summary prompt for pymdp_pomdp_agent.md (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:20,739 - __main__ - INFO - Successfully generated summary for pymdp_pomdp_agent.md. Saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step/pymdp_pomdp_agent/pymdp_pomdp_agent_summary.txt (Size: 1610 bytes)\n2025-06-07 12:29:20,740 - __main__ - INFO - Task 2: Generating comprehensive analysis for pymdp_pomdp_agent.md...\n2025-06-07 12:29:20,740 - __main__ - DEBUG - Comprehensive analysis prompt for pymdp_pomdp_agent.md (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:42,239 - __main__ - INFO - Successfully generated comprehensive analysis for pymdp_pomdp_agent.md. Saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step/pymdp_pomdp_agent/pymdp_pomdp_agent_comprehensive_analysis.json (Size: 5285 bytes)\n2025-06-07 12:29:42,239 - __main__ - INFO - Tasks 3 & 4: Generating Q&A for pymdp_pomdp_agent.md...\n2025-06-07 12:29:42,239 - __main__ - DEBUG - Question generation prompt for pymdp_pomdp_agent.md (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:45,341 - __main__ - INFO - Successfully generated 5 questions for pymdp_pomdp_agent.md.\n2025-06-07 12:29:45,341 - __main__ - INFO - Answering 5 generated questions for pymdp_pomdp_agent.md...\n2025-06-07 12:29:45,341 - __main__ - DEBUG - Answering question 1/5: 'What are the implications of having multiple observation modalities on the decision-making process of the Multifactor PyMDP Agent?'\n2025-06-07 12:29:49,787 - __main__ - DEBUG - Answer for 'What are the implications of having multiple observation modalities on the decision-making process of the Multifactor PyMDP Agent?': The GNN file indicates that the Multifactor PyMDP Agent utilizes multiple observation modalities\u2014spe...\n2025-06-07 12:29:49,787 - __main__ - DEBUG - Answering question 2/5: 'How does the control over the 'decision_state' factor influence the overall performance and adaptability of the agent in varying environments?'\n2025-06-07 12:29:51,339 - __main__ - DEBUG - Answer for 'How does the control over the 'decision_state' factor influence the overall performance and adaptability of the agent in varying environments?': The GNN file does not provide sufficient information to directly answer how control over the 'decisi...\n2025-06-07 12:29:51,339 - __main__ - DEBUG - Answering question 3/5: 'In what ways do the defined likelihood matrices (A_m) and transition matrices (B_f) affect the agent's ability to infer states and select actions?'\n2025-06-07 12:29:56,949 - __main__ - DEBUG - Answer for 'In what ways do the defined likelihood matrices (A_m) and transition matrices (B_f) affect the agent's ability to infer states and select actions?': The defined likelihood matrices (A_m) and transition matrices (B_f) play crucial roles in the agent'...\n2025-06-07 12:29:56,949 - __main__ - DEBUG - Answering question 4/5: 'What assumptions are made regarding the independence of hidden state factors in this model, and how might these assumptions impact the agent's learning and decision-making?'\n2025-06-07 12:30:00,432 - __main__ - DEBUG - Answer for 'What assumptions are made regarding the independence of hidden state factors in this model, and how might these assumptions impact the agent's learning and decision-making?': The GNN file content does not explicitly state any assumptions regarding the independence of hidden ...\n2025-06-07 12:30:00,432 - __main__ - DEBUG - Answering question 5/5: 'How does the structure of the initial parameterization affect the convergence and stability of the agent's learning process during simulations?'\n2025-06-07 12:30:02,406 - __main__ - DEBUG - Answer for 'How does the structure of the initial parameterization affect the convergence and stability of the agent's learning process during simulations?': The GNN file does not contain sufficient information to directly assess how the structure of the ini...\n2025-06-07 12:30:02,407 - __main__ - INFO - Successfully generated and answered questions for pymdp_pomdp_agent.md. Saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step/pymdp_pomdp_agent/pymdp_pomdp_agent_qa.json (Size: 5758 bytes)\n2025-06-07 12:30:02,407 - __main__ - INFO - Finished LLM processing for GNN file: pymdp_pomdp_agent.md\n2025-06-07 12:30:02,407 - __main__ - INFO - --- Processing GNN file: rxinfer_multiagent_gnn.md ---\n2025-06-07 12:30:02,407 - __main__ - INFO - Processing GNN file with LLM: rxinfer_multiagent_gnn.md\n2025-06-07 12:30:02,407 - __main__ - INFO - Task 1: Generating summary for rxinfer_multiagent_gnn.md...\n2025-06-07 12:30:02,407 - __main__ - DEBUG - Summary prompt for rxinfer_multiagent_gnn.md (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:09,275 - __main__ - INFO - Successfully generated summary for rxinfer_multiagent_gnn.md. Saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step/rxinfer_multiagent_gnn/rxinfer_multiagent_gnn_summary.txt (Size: 1921 bytes)\n2025-06-07 12:30:09,275 - __main__ - INFO - Task 2: Generating comprehensive analysis for rxinfer_multiagent_gnn.md...\n2025-06-07 12:30:09,275 - __main__ - DEBUG - Comprehensive analysis prompt for rxinfer_multiagent_gnn.md (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:59,215 - __main__ - INFO - Successfully generated comprehensive analysis for rxinfer_multiagent_gnn.md. Saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/llm_processing_step/rxinfer_multiagent_gnn/rxinfer_multiagent_gnn_comprehensive_analysis.json (Size: 5355 bytes)\n2025-06-07 12:30:59,215 - __main__ - INFO - Tasks 3 & 4: Generating Q&A for rxinfer_multiagent_gnn.md...\n2025-06-07 12:30:59,215 - __main__ - DEBUG - Question generation prompt for rxinfer_multiagent_gnn.md (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:01,666 - __main__ - INFO - Successfully generated 5 questions for rxinfer_multiagent_gnn.md.\n2025-06-07 12:31:01,666 - __main__ - INFO - Answering 5 generated questions for rxinfer_multiagent_gnn.md...\n2025-06-07 12:31:01,666 - __main__ - DEBUG - Answering question 1/5: 'How does the model handle the trade-off between goal-directed behavior and obstacle avoidance in multi-agent trajectory planning?'\n2025-06-07 12:31:05,347 - __main__ - DEBUG - Answer for 'How does the model handle the trade-off between goal-directed behavior and obstacle avoidance in multi-agent trajectory planning?': The model handles the trade-off between goal-directed behavior and obstacle avoidance in multi-agent...\n2025-06-07 12:31:05,347 - __main__ - DEBUG - Answering question 2/5: 'What is the significance of the softmin_temperature parameter, and how does it influence the agents' decision-making process?'\n2025-06-07 12:31:06,687 - __main__ - DEBUG - Answer for 'What is the significance of the softmin_temperature parameter, and how does it influence the agents' decision-making process?': The GNN file does not provide specific information about the significance of the softmin_temperature...\n2025-06-07 12:31:06,687 - __main__ - DEBUG - Answering question 3/5: 'In what ways do the specified variances (initial_state_variance, control_variance, and goal_constraint_variance) impact the performance of the trajectory planning model?'\n2025-06-07 12:31:11,294 - __main__ - DEBUG - Answer for 'In what ways do the specified variances (initial_state_variance, control_variance, and goal_constraint_variance) impact the performance of the trajectory planning model?': The GNN file specifies the following variances that impact the performance of the trajectory plannin...\n2025-06-07 12:31:11,294 - __main__ - DEBUG - Answering question 4/5: 'How are the collision avoidance constraints structured in the model, and what assumptions are made about agent interactions?'\n",
"stderr": "Failed to load MCP module src.export.mcp: No module named 'src.export.structured_data_exporters'\n2025-06-07 12:29:13,973 - root - WARNING - Could not directly import MCPTool from src.mcp. This might be okay if loaded by the main MCP system.\n2025-06-07 12:29:13,977 - src.render.mcp - INFO - Render module MCP tools registered.\n2025-06-07 12:29:13,977 - src.llm.mcp - INFO - Successfully imported MCPTool and MCPSDKNotFoundError from src.mcp.mcp in initialize_llm_module.\n2025-06-07 12:29:13,978 - llm.llm_operations - INFO - OpenAI API key loaded successfully.\n2025-06-07 12:29:13,978 - src.llm.mcp - INFO - LLM API Key loaded successfully.\n2025-06-07 12:29:13,978 - src.llm.mcp - INFO - Registered MCP tool: llm.summarize_gnn_file\n2025-06-07 12:29:13,978 - src.llm.mcp - INFO - Registered MCP tool: llm.explain_gnn_file\n2025-06-07 12:29:13,978 - src.llm.mcp - INFO - Registered MCP tool: llm.generate_professional_summary\n2025-06-07 12:29:13,978 - src.llm.mcp - INFO - LLM module MCP tools registration process completed.\n2025-06-07 12:29:13,978 - src.mcp.sympy_mcp - INFO - Successfully registered SymPy MCP tools\n2025-06-07 12:29:13,978 - mcp - INFO - Successfully registered SymPy MCP tools\n2025-06-07 12:29:13,978 - mcp - INFO - MCP system initialized using project's internal MCP components. SDK Status: Using project's internal MCP implementation.\n2025-06-07 12:29:13,978 - __main__ - INFO - Successfully imported llm_operations, llm_mcp, and mcp_instance.\n2025-06-07 12:29:13,979 - src.llm.mcp - INFO - Attempting to ensure LLM tools are registered with MCP.\n2025-06-07 12:29:13,979 - src.llm.mcp - INFO - Successfully imported MCPTool and MCPSDKNotFoundError from src.mcp.mcp in initialize_llm_module.\n2025-06-07 12:29:13,980 - llm.llm_operations - INFO - OpenAI API key loaded successfully.\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - LLM API Key loaded successfully.\n2025-06-07 12:29:13,980 - mcp - WARNING - Tool 'llm.summarize_gnn_file' already registered. Overwriting.\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - Registered MCP tool: llm.summarize_gnn_file\n2025-06-07 12:29:13,980 - mcp - WARNING - Tool 'llm.explain_gnn_file' already registered. Overwriting.\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - Registered MCP tool: llm.explain_gnn_file\n2025-06-07 12:29:13,980 - mcp - WARNING - Tool 'llm.generate_professional_summary' already registered. Overwriting.\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - Registered MCP tool: llm.generate_professional_summary\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - LLM module MCP tools registration process completed.\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - ensure_llm_tools_registered call completed.\n2025-06-07 12:29:13,981 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:13,981 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:13,981 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:14,004 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:20,719 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:20,739 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1609 chars. Token usage: Prompt=2572, Completion=351, Total=2923\n2025-06-07 12:29:20,739 - src.llm.llm_operations - DEBUG - LLM Response preview: ### Summary of the Multifactor PyMDP Agent GNN Model\n\n**Model Name:** Multifactor PyMDP Agent v1\n\n**Purpose:** This model represents a Generalized Notation Notation (GNN) framework for a PyMDP agent that integrates multiple observation modalities and hidden state factors to facilitate decision-making in an Active Inference paradigm.\n\n**Key Components:**\n1. **Observation Modalities:**\n - **State Observation:** 3 outcomes\n - **Reward:** 3 outcomes\n - **Decision Proprioceptive:** 3 outcomes\n\n...\n2025-06-07 12:29:20,740 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:20,740 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 3000, Temperature: 0.7)\n2025-06-07 12:29:20,740 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:20,751 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:42,236 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:42,238 - src.llm.llm_operations - INFO - LLM call successful. Response length: 4533 chars. Token usage: Prompt=2625, Completion=1264, Total=3889\n2025-06-07 12:29:42,238 - src.llm.llm_operations - DEBUG - LLM Response preview: ```json\n{\n \"model_purpose\": \"The model represents a Multifactor PyMDP agent that operates using multiple observation modalities and hidden state factors, aimed at decision-making under uncertainty through active inference.\",\n \"key_components\": {\n \"hidden_states\": {\n \"reward_level\": {\n \"states\": 2,\n \"description\": \"Represents the level of reward the agent perceives.\"\n },\n \"decision_state\": {\n \"states\": 3,\n \"description\": \"Represents the internal sta...\n2025-06-07 12:29:42,239 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:42,239 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:42,239 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:42,246 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:45,334 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:45,341 - src.llm.llm_operations - INFO - LLM call successful. Response length: 787 chars. Token usage: Prompt=2570, Completion=146, Total=2716\n2025-06-07 12:29:45,341 - src.llm.llm_operations - DEBUG - LLM Response preview: ```json\n[\n \"What are the implications of having multiple observation modalities on the decision-making process of the Multifactor PyMDP Agent?\",\n \"How does the control over the 'decision_state' factor influence the overall performance and adaptability of the agent in varying environments?\",\n \"In what ways do the defined likelihood matrices (A_m) and transition matrices (B_f) affect the agent's ability to infer states and select actions?\",\n \"What assumptions are made regarding the ind...\n2025-06-07 12:29:45,341 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:45,342 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:45,342 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:45,347 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:49,784 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:49,786 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1287 chars. Token usage: Prompt=2598, Completion=227, Total=2825\n2025-06-07 12:29:49,787 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file indicates that the Multifactor PyMDP Agent utilizes multiple observation modalities\u2014specifically \"state_observation,\" \"reward,\" and \"decision_proprioceptive\"\u2014each with three outcomes. The implications of having these multiple modalities on the decision-making process include:\n\n1. **Enhanced Information Capture**: The agent can gather diverse types of information from the environment, which can lead to a more comprehensive understanding of the current state and context.\n\n2. **Improve...\n2025-06-07 12:29:49,787 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:49,787 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:49,787 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:49,819 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:51,338 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:51,339 - src.llm.llm_operations - INFO - LLM call successful. Response length: 409 chars. Token usage: Prompt=2599, Completion=68, Total=2667\n2025-06-07 12:29:51,339 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file does not provide sufficient information to directly answer how control over the 'decision_state' factor influences the overall performance and adaptability of the agent in varying environments. While it mentions that the 'decision_state' factor is controllable with three possible actions, it lacks specific details on how these actions impact performance and adaptability in different scenarios....\n2025-06-07 12:29:51,339 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:51,339 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:51,339 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:51,344 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:56,948 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:56,949 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1414 chars. Token usage: Prompt=2606, Completion=298, Total=2904\n2025-06-07 12:29:56,949 - src.llm.llm_operations - DEBUG - LLM Response preview: The defined likelihood matrices (A_m) and transition matrices (B_f) play crucial roles in the agent's ability to infer states and select actions:\n\n1. **Likelihood Matrices (A_m)**:\n - The A_m matrices define the probabilities of observing certain outcomes given specific hidden states. Each modality (A_m0, A_m1, A_m2) influences how the agent updates its beliefs about the hidden states (s_f0 and s_f1) based on observed data (o_m0, o_m1, o_m2).\n - The agent uses these likelihoods to infer the ...\n2025-06-07 12:29:56,949 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:56,949 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:56,949 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:56,955 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:30:00,431 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:30:00,432 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1016 chars. Token usage: Prompt=2604, Completion=186, Total=2790\n2025-06-07 12:30:00,432 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file content does not explicitly state any assumptions regarding the independence of hidden state factors in the model. However, the structure implies that the hidden state factors \"reward_level\" (s_f0) and \"decision_state\" (s_f1) are treated separately in the transition matrices (B_f0 and B_f1) and observation likelihoods (A_m matrices). \n\nThis treatment suggests an assumption of conditional independence between the hidden state factors given the actions and observations. Such an assump...\n2025-06-07 12:30:00,432 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:30:00,432 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:30:00,432 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:30:00,439 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:30:02,403 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:30:02,405 - src.llm.llm_operations - INFO - LLM call successful. Response length: 565 chars. Token usage: Prompt=2598, Completion=94, Total=2692\n2025-06-07 12:30:02,405 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file does not contain sufficient information to directly assess how the structure of the initial parameterization affects the convergence and stability of the agent's learning process during simulations. The document provides details about the parameterization itself, such as the likelihood matrices, transition matrices, and preferences, but it lacks specific insights or empirical data regarding their impact on learning dynamics or stability. Therefore, I cannot provide a definitive answ...\n2025-06-07 12:30:02,407 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:02,407 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:30:02,407 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:02,413 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:30:09,272 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:30:09,274 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1921 chars. Token usage: Prompt=2641, Completion=365, Total=3006\n2025-06-07 12:30:09,274 - src.llm.llm_operations - DEBUG - LLM Response preview: ### Summary of the GNN Model: Multi-agent Trajectory Planning\n\n**Model Name:** Multi-agent Trajectory Planning\n\n**Purpose:** This GNN model represents a multi-agent trajectory planning scenario designed for use with RxInfer.jl. It aims to simulate how multiple agents navigate a 2D environment while adhering to various constraints, including obstacle avoidance and collision avoidance, to reach designated targets.\n\n**Key Components:**\n\n1. **State Space Model:**\n - The model incorporates a state ...\n2025-06-07 12:30:09,275 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:09,276 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 3000, Temperature: 0.7)\n2025-06-07 12:30:09,276 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:09,305 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:30:39,420 - openai._base_client - INFO - Retrying request to /chat/completions in 0.497001 seconds\n2025-06-07 12:30:59,209 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:30:59,215 - src.llm.llm_operations - INFO - LLM call successful. Response length: 4783 chars. Token usage: Prompt=2694, Completion=1061, Total=3755\n2025-06-07 12:30:59,215 - src.llm.llm_operations - DEBUG - LLM Response preview: ```json\n{\n \"model_purpose\": \"This GNN model is designed for multi-agent trajectory planning using the RxInfer.jl framework. It aims to simulate and optimize the trajectories of multiple agents in a 2D environment while considering obstacle avoidance, goal-directed behavior, and collision avoidance among agents.\",\n \"key_components\": {\n \"state_space_model\": {\n \"description\": \"Defines the dynamics of the agents in the environment.\",\n \"parameters\": {\n \"A\": \"State transition mat...\n2025-06-07 12:30:59,215 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:59,215 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:30:59,215 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:59,220 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:31:01,665 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:31:01,666 - src.llm.llm_operations - INFO - LLM call successful. Response length: 760 chars. Token usage: Prompt=2639, Completion=140, Total=2779\n2025-06-07 12:31:01,666 - src.llm.llm_operations - DEBUG - LLM Response preview: ```json\n[\n \"How does the model handle the trade-off between goal-directed behavior and obstacle avoidance in multi-agent trajectory planning?\",\n \"What is the significance of the softmin_temperature parameter, and how does it influence the agents' decision-making process?\",\n \"In what ways do the specified variances (initial_state_variance, control_variance, and goal_constraint_variance) impact the performance of the trajectory planning model?\",\n \"How are the collision avoidance constr...\n2025-06-07 12:31:01,666 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:01,666 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:31:01,666 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:01,672 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:31:05,345 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:31:05,347 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1108 chars. Token usage: Prompt=2665, Completion=198, Total=2863\n2025-06-07 12:31:05,347 - src.llm.llm_operations - DEBUG - LLM Response preview: The model handles the trade-off between goal-directed behavior and obstacle avoidance in multi-agent trajectory planning through the use of probabilistic constraints. Specifically, it employs the following mechanisms:\n\n1. **Goal Constraints**: The model incorporates a goal-directed behavior component where the final position \\( x_T \\) of an agent is modeled to approach the goal with a distribution characterized by the variance \\( \\text{goal\\_constraint\\_variance} \\). This encourages agents to mo...\n2025-06-07 12:31:05,347 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:05,347 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:31:05,347 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:05,365 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:31:06,684 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:31:06,686 - src.llm.llm_operations - INFO - LLM call successful. Response length: 258 chars. Token usage: Prompt=2667, Completion=46, Total=2713\n2025-06-07 12:31:06,686 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file does not provide specific information about the significance of the softmin_temperature parameter or its influence on the agents' decision-making process. Therefore, it is not possible to answer the question based solely on the provided content....\n2025-06-07 12:31:06,687 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:06,687 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:31:06,687 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:06,693 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:31:11,290 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:31:11,294 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1425 chars. Token usage: Prompt=2677, Completion=247, Total=2924\n2025-06-07 12:31:11,294 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file specifies the following variances that impact the performance of the trajectory planning model:\n\n1. **Initial State Variance (`initial_state_variance`)**: This variance affects the uncertainty in the initial positions of the agents. A higher initial state variance leads to greater uncertainty in the starting conditions, which can result in less accurate trajectory predictions and potential difficulties in reaching the desired goals.\n\n2. **Control Variance (`control_variance`)**: Thi...\n2025-06-07 12:31:11,294 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:11,294 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:31:11,294 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:11,302 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\nFailed to load MCP module src.export.mcp: No module named 'src.export.structured_data_exporters'\n2025-06-07 12:29:13,973 - root - WARNING - Could not directly import MCPTool from src.mcp. This might be okay if loaded by the main MCP system.\n2025-06-07 12:29:13,977 - src.render.mcp - INFO - Render module MCP tools registered.\n2025-06-07 12:29:13,977 - src.llm.mcp - INFO - Successfully imported MCPTool and MCPSDKNotFoundError from src.mcp.mcp in initialize_llm_module.\n2025-06-07 12:29:13,978 - llm.llm_operations - INFO - OpenAI API key loaded successfully.\n2025-06-07 12:29:13,978 - src.llm.mcp - INFO - LLM API Key loaded successfully.\n2025-06-07 12:29:13,978 - src.llm.mcp - INFO - Registered MCP tool: llm.summarize_gnn_file\n2025-06-07 12:29:13,978 - src.llm.mcp - INFO - Registered MCP tool: llm.explain_gnn_file\n2025-06-07 12:29:13,978 - src.llm.mcp - INFO - Registered MCP tool: llm.generate_professional_summary\n2025-06-07 12:29:13,978 - src.llm.mcp - INFO - LLM module MCP tools registration process completed.\n2025-06-07 12:29:13,978 - src.mcp.sympy_mcp - INFO - Successfully registered SymPy MCP tools\n2025-06-07 12:29:13,978 - mcp - INFO - Successfully registered SymPy MCP tools\n2025-06-07 12:29:13,978 - mcp - INFO - MCP system initialized using project's internal MCP components. SDK Status: Using project's internal MCP implementation.\n2025-06-07 12:29:13,978 - __main__ - INFO - Successfully imported llm_operations, llm_mcp, and mcp_instance.\n2025-06-07 12:29:13,979 - src.llm.mcp - INFO - Attempting to ensure LLM tools are registered with MCP.\n2025-06-07 12:29:13,979 - src.llm.mcp - INFO - Successfully imported MCPTool and MCPSDKNotFoundError from src.mcp.mcp in initialize_llm_module.\n2025-06-07 12:29:13,980 - llm.llm_operations - INFO - OpenAI API key loaded successfully.\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - LLM API Key loaded successfully.\n2025-06-07 12:29:13,980 - mcp - WARNING - Tool 'llm.summarize_gnn_file' already registered. Overwriting.\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - Registered MCP tool: llm.summarize_gnn_file\n2025-06-07 12:29:13,980 - mcp - WARNING - Tool 'llm.explain_gnn_file' already registered. Overwriting.\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - Registered MCP tool: llm.explain_gnn_file\n2025-06-07 12:29:13,980 - mcp - WARNING - Tool 'llm.generate_professional_summary' already registered. Overwriting.\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - Registered MCP tool: llm.generate_professional_summary\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - LLM module MCP tools registration process completed.\n2025-06-07 12:29:13,980 - src.llm.mcp - INFO - ensure_llm_tools_registered call completed.\n2025-06-07 12:29:13,981 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:13,981 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:13,981 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:14,004 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:20,719 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:20,739 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1609 chars. Token usage: Prompt=2572, Completion=351, Total=2923\n2025-06-07 12:29:20,739 - src.llm.llm_operations - DEBUG - LLM Response preview: ### Summary of the Multifactor PyMDP Agent GNN Model\n\n**Model Name:** Multifactor PyMDP Agent v1\n\n**Purpose:** This model represents a Generalized Notation Notation (GNN) framework for a PyMDP agent that integrates multiple observation modalities and hidden state factors to facilitate decision-making in an Active Inference paradigm.\n\n**Key Components:**\n1. **Observation Modalities:**\n - **State Observation:** 3 outcomes\n - **Reward:** 3 outcomes\n - **Decision Proprioceptive:** 3 outcomes\n\n...\n2025-06-07 12:29:20,740 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:20,740 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 3000, Temperature: 0.7)\n2025-06-07 12:29:20,740 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:20,751 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:42,236 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:42,238 - src.llm.llm_operations - INFO - LLM call successful. Response length: 4533 chars. Token usage: Prompt=2625, Completion=1264, Total=3889\n2025-06-07 12:29:42,238 - src.llm.llm_operations - DEBUG - LLM Response preview: ```json\n{\n \"model_purpose\": \"The model represents a Multifactor PyMDP agent that operates using multiple observation modalities and hidden state factors, aimed at decision-making under uncertainty through active inference.\",\n \"key_components\": {\n \"hidden_states\": {\n \"reward_level\": {\n \"states\": 2,\n \"description\": \"Represents the level of reward the agent perceives.\"\n },\n \"decision_state\": {\n \"states\": 3,\n \"description\": \"Represents the internal sta...\n2025-06-07 12:29:42,239 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:42,239 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:42,239 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:42,246 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:45,334 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:45,341 - src.llm.llm_operations - INFO - LLM call successful. Response length: 787 chars. Token usage: Prompt=2570, Completion=146, Total=2716\n2025-06-07 12:29:45,341 - src.llm.llm_operations - DEBUG - LLM Response preview: ```json\n[\n \"What are the implications of having multiple observation modalities on the decision-making process of the Multifactor PyMDP Agent?\",\n \"How does the control over the 'decision_state' factor influence the overall performance and adaptability of the agent in varying environments?\",\n \"In what ways do the defined likelihood matrices (A_m) and transition matrices (B_f) affect the agent's ability to infer states and select actions?\",\n \"What assumptions are made regarding the ind...\n2025-06-07 12:29:45,341 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:45,342 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:45,342 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:45,347 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:49,784 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:49,786 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1287 chars. Token usage: Prompt=2598, Completion=227, Total=2825\n2025-06-07 12:29:49,787 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file indicates that the Multifactor PyMDP Agent utilizes multiple observation modalities\u2014specifically \"state_observation,\" \"reward,\" and \"decision_proprioceptive\"\u2014each with three outcomes. The implications of having these multiple modalities on the decision-making process include:\n\n1. **Enhanced Information Capture**: The agent can gather diverse types of information from the environment, which can lead to a more comprehensive understanding of the current state and context.\n\n2. **Improve...\n2025-06-07 12:29:49,787 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:49,787 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:49,787 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:49,819 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:51,338 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:51,339 - src.llm.llm_operations - INFO - LLM call successful. Response length: 409 chars. Token usage: Prompt=2599, Completion=68, Total=2667\n2025-06-07 12:29:51,339 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file does not provide sufficient information to directly answer how control over the 'decision_state' factor influences the overall performance and adaptability of the agent in varying environments. While it mentions that the 'decision_state' factor is controllable with three possible actions, it lacks specific details on how these actions impact performance and adaptability in different scenarios....\n2025-06-07 12:29:51,339 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:51,339 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:51,339 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:51,344 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:29:56,948 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:29:56,949 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1414 chars. Token usage: Prompt=2606, Completion=298, Total=2904\n2025-06-07 12:29:56,949 - src.llm.llm_operations - DEBUG - LLM Response preview: The defined likelihood matrices (A_m) and transition matrices (B_f) play crucial roles in the agent's ability to infer states and select actions:\n\n1. **Likelihood Matrices (A_m)**:\n - The A_m matrices define the probabilities of observing certain outcomes given specific hidden states. Each modality (A_m0, A_m1, A_m2) influences how the agent updates its beliefs about the hidden states (s_f0 and s_f1) based on observed data (o_m0, o_m1, o_m2).\n - The agent uses these likelihoods to infer the ...\n2025-06-07 12:29:56,949 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:56,949 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:29:56,949 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:29:56,955 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:30:00,431 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:30:00,432 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1016 chars. Token usage: Prompt=2604, Completion=186, Total=2790\n2025-06-07 12:30:00,432 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file content does not explicitly state any assumptions regarding the independence of hidden state factors in the model. However, the structure implies that the hidden state factors \"reward_level\" (s_f0) and \"decision_state\" (s_f1) are treated separately in the transition matrices (B_f0 and B_f1) and observation likelihoods (A_m matrices). \n\nThis treatment suggests an assumption of conditional independence between the hidden state factors given the actions and observations. Such an assump...\n2025-06-07 12:30:00,432 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:30:00,432 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:30:00,432 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (pymdp_pomdp_agent.md):\n# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.\n\n## GNNSection\nMultifactorPyMDPAgent\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMultifactor PyMDP Agent v1\n\n## ModelAnnotation\nThis model represents a PyMDP agent with multiple ob...\n2025-06-07 12:30:00,439 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:30:02,403 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:30:02,405 - src.llm.llm_operations - INFO - LLM call successful. Response length: 565 chars. Token usage: Prompt=2598, Completion=94, Total=2692\n2025-06-07 12:30:02,405 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file does not contain sufficient information to directly assess how the structure of the initial parameterization affects the convergence and stability of the agent's learning process during simulations. The document provides details about the parameterization itself, such as the likelihood matrices, transition matrices, and preferences, but it lacks specific insights or empirical data regarding their impact on learning dynamics or stability. Therefore, I cannot provide a definitive answ...\n2025-06-07 12:30:02,407 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:02,407 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:30:02,407 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:02,413 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:30:09,272 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:30:09,274 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1921 chars. Token usage: Prompt=2641, Completion=365, Total=3006\n2025-06-07 12:30:09,274 - src.llm.llm_operations - DEBUG - LLM Response preview: ### Summary of the GNN Model: Multi-agent Trajectory Planning\n\n**Model Name:** Multi-agent Trajectory Planning\n\n**Purpose:** This GNN model represents a multi-agent trajectory planning scenario designed for use with RxInfer.jl. It aims to simulate how multiple agents navigate a 2D environment while adhering to various constraints, including obstacle avoidance and collision avoidance, to reach designated targets.\n\n**Key Components:**\n\n1. **State Space Model:**\n - The model incorporates a state ...\n2025-06-07 12:30:09,275 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:09,276 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 3000, Temperature: 0.7)\n2025-06-07 12:30:09,276 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:09,305 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:30:39,420 - openai._base_client - INFO - Retrying request to /chat/completions in 0.497001 seconds\n2025-06-07 12:30:59,209 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:30:59,215 - src.llm.llm_operations - INFO - LLM call successful. Response length: 4783 chars. Token usage: Prompt=2694, Completion=1061, Total=3755\n2025-06-07 12:30:59,215 - src.llm.llm_operations - DEBUG - LLM Response preview: ```json\n{\n \"model_purpose\": \"This GNN model is designed for multi-agent trajectory planning using the RxInfer.jl framework. It aims to simulate and optimize the trajectories of multiple agents in a 2D environment while considering obstacle avoidance, goal-directed behavior, and collision avoidance among agents.\",\n \"key_components\": {\n \"state_space_model\": {\n \"description\": \"Defines the dynamics of the agents in the environment.\",\n \"parameters\": {\n \"A\": \"State transition mat...\n2025-06-07 12:30:59,215 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:59,215 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:30:59,215 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:30:59,220 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:31:01,665 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:31:01,666 - src.llm.llm_operations - INFO - LLM call successful. Response length: 760 chars. Token usage: Prompt=2639, Completion=140, Total=2779\n2025-06-07 12:31:01,666 - src.llm.llm_operations - DEBUG - LLM Response preview: ```json\n[\n \"How does the model handle the trade-off between goal-directed behavior and obstacle avoidance in multi-agent trajectory planning?\",\n \"What is the significance of the softmin_temperature parameter, and how does it influence the agents' decision-making process?\",\n \"In what ways do the specified variances (initial_state_variance, control_variance, and goal_constraint_variance) impact the performance of the trajectory planning model?\",\n \"How are the collision avoidance constr...\n2025-06-07 12:31:01,666 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:01,666 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:31:01,666 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:01,672 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:31:05,345 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:31:05,347 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1108 chars. Token usage: Prompt=2665, Completion=198, Total=2863\n2025-06-07 12:31:05,347 - src.llm.llm_operations - DEBUG - LLM Response preview: The model handles the trade-off between goal-directed behavior and obstacle avoidance in multi-agent trajectory planning through the use of probabilistic constraints. Specifically, it employs the following mechanisms:\n\n1. **Goal Constraints**: The model incorporates a goal-directed behavior component where the final position \\( x_T \\) of an agent is modeled to approach the goal with a distribution characterized by the variance \\( \\text{goal\\_constraint\\_variance} \\). This encourages agents to mo...\n2025-06-07 12:31:05,347 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:05,347 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:31:05,347 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:05,365 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:31:06,684 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:31:06,686 - src.llm.llm_operations - INFO - LLM call successful. Response length: 258 chars. Token usage: Prompt=2667, Completion=46, Total=2713\n2025-06-07 12:31:06,686 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file does not provide specific information about the significance of the softmin_temperature parameter or its influence on the agents' decision-making process. Therefore, it is not possible to answer the question based solely on the provided content....\n2025-06-07 12:31:06,687 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:06,687 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:31:06,687 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:06,693 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n2025-06-07 12:31:11,290 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n2025-06-07 12:31:11,294 - src.llm.llm_operations - INFO - LLM call successful. Response length: 1425 chars. Token usage: Prompt=2677, Completion=247, Total=2924\n2025-06-07 12:31:11,294 - src.llm.llm_operations - DEBUG - LLM Response preview: The GNN file specifies the following variances that impact the performance of the trajectory planning model:\n\n1. **Initial State Variance (`initial_state_variance`)**: This variance affects the uncertainty in the initial positions of the agents. A higher initial state variance leads to greater uncertainty in the starting conditions, which can result in less accurate trajectory predictions and potential difficulties in reaching the desired goals.\n\n2. **Control Variance (`control_variance`)**: Thi...\n2025-06-07 12:31:11,294 - src.llm.llm_operations - DEBUG - Constructed prompt: GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:11,294 - src.llm.llm_operations - INFO - Sending prompt to OpenAI model: gpt-4o-mini (Timeout: 30.0s, Max Tokens: 1000, Temperature: 0.7)\n2025-06-07 12:31:11,294 - src.llm.llm_operations - DEBUG - Full prompt for gpt-4o-mini (first 500 chars): GNN File Content (rxinfer_multiagent_gnn.md):\n# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.\n\n## GNNSection\nRxInferMultiAgentTrajectoryPlanning\n\n## GNNVersionAndFlags\nGNN v1\n\n## ModelName\nMulti-agent Trajectory Planning\n\n## ModelAnnotation\nThis model represent...\n2025-06-07 12:31:11,302 - src.llm.llm_operations - INFO - Attempting to call OpenAI API with model gpt-4o-mini...\n"
},
{
"step_number": 12,
"script_name": "12_discopy.py",
"status": "SUCCESS",
"start_time": "2025-06-07T12:31:12.611760",
"end_time": "2025-06-07T12:31:13.854321",
"duration_seconds": 1.242561,
"details": "",
"stdout": "2025-06-07 12:31:13,253 - __main__ - INFO - Starting pipeline step: 12_discopy.py - GNN to DisCoPy Transformation\n2025-06-07 12:31:13,253 - __main__ - INFO - Reading GNN files from: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples\n2025-06-07 12:31:13,253 - __main__ - INFO - DisCoPy outputs will be saved in: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/discopy_gnn\n2025-06-07 12:31:13,254 - __main__ - INFO - Found 2 GNN files to process.\n2025-06-07 12:31:13,254 - __main__ - INFO - Processing GNN file for DisCoPy: pymdp_pomdp_agent.md\n2025-06-07 12:31:13,265 - __main__ - DEBUG - Attempting to draw diagram to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/discopy_gnn/pymdp_pomdp_agent_diagram.png\n2025-06-07 12:31:13,625 - __main__ - INFO - Saved DisCoPy diagram visualization to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/discopy_gnn/pymdp_pomdp_agent_diagram.png\n2025-06-07 12:31:13,625 - __main__ - INFO - Processing GNN file for DisCoPy: rxinfer_multiagent_gnn.md\n2025-06-07 12:31:13,627 - __main__ - DEBUG - Attempting to draw diagram to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/discopy_gnn/rxinfer_multiagent_gnn_diagram.png\n2025-06-07 12:31:13,724 - __main__ - INFO - Saved DisCoPy diagram visualization to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/discopy_gnn/rxinfer_multiagent_gnn_diagram.png\n2025-06-07 12:31:13,724 - __main__ - INFO - Finished processing 2 GNN files. 2 diagrams generated successfully.\n2025-06-07 12:31:13,724 - __main__ - INFO - All processed GNN files yielded DisCoPy diagrams successfully.\n",
"stderr": "WARNING:2025-06-07 12:31:13,238:jax._src.xla_bridge:909: An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\nConnection from 's_f0,s_f1,u_f1' to 'B_f0,B_f1' (Box dom=Dim(2, 3), cod=Dim(2, 2, 3, 3, 3)) does not directly chain with previous diagram codomain (Dim(3, 3, 3)). Appending in parallel (basic).\nConnection from 'B_f0,B_f1' to 's_prime_f0,s_prime_f1' (Box dom=Dim(2, 2, 3, 3, 3), cod=Dim(2, 3)) does not directly chain with previous diagram codomain (Dim(3, 3, 3, 2, 2, 3, 3, 3)). Appending in parallel (basic).\nConnection from 'C_m0,C_m1,C_m2' to 'G' (Box dom=Dim(3, 3, 3), cod=Dim(1)) does not directly chain with previous diagram codomain (Dim(3, 3, 3, 2, 2, 3, 3, 3, 2, 3)). Appending in parallel (basic).\nConnection from 'G' to '\u03c0_f1' (Box dom=Dim(1), cod=Dim(3)) does not directly chain with previous diagram codomain (Dim(3, 3, 3, 2, 2, 3, 3, 3, 2, 3)). Appending in parallel (basic).\nConnection from '\u03c0_f1' to 'u_f1' (Box dom=Dim(3), cod=Dim(1)) does not directly chain with previous diagram codomain (Dim(3, 3, 3, 2, 2, 3, 3, 3, 2, 3, 3)). Appending in parallel (basic).\nUnknown variable 'state_space_model' (not in dims_map) in connection: '(A, B, C) > state_space_model'. Skipping connection.\nUnknown variable 'state_space_model' (not in dims_map) in connection: '(state_space_model, initial_state_variance, control_variance) > agent_trajectories'. Skipping connection.\nUnknown variable 'agent_trajectories' (not in dims_map) in connection: '(agent_trajectories, goal_constraint_variance) > goal_directed_behavior'. Skipping connection.\nUnknown variable 'agent_trajectories' (not in dims_map) in connection: '(agent_trajectories, gamma, gamma_shape, gamma_scale_factor) > obstacle_avoidance'. Skipping connection.\nUnknown variable 'agent_trajectories' (not in dims_map) in connection: '(agent_trajectories, nr_agents) > collision_avoidance'. Skipping connection.\nUnknown variable 'goal_directed_behavior' (not in dims_map) in connection: '(goal_directed_behavior, obstacle_avoidance, collision_avoidance) > planning_system'. Skipping connection.\n"
},
{
"step_number": 14,
"script_name": "14_site.py",
"status": "SUCCESS",
"start_time": "2025-06-07T12:31:13.854365",
"end_time": "2025-06-07T12:31:14.017490",
"duration_seconds": 0.163125,
"details": "",
"stdout": "2025-06-07 12:31:13,905 - __main__ - INFO - Starting pipeline step: 15_site.py - Generate HTML Summary Site\n2025-06-07 12:31:13,905 - __main__ - INFO - Reading from pipeline output directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output\n2025-06-07 12:31:13,905 - __main__ - INFO - Generated site will be saved as: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_pipeline_summary_site.html\n2025-06-07 12:31:13,905 - __main__ - DEBUG - Calling generate_html_report with output_dir='/home/trim/Documents/GitHub/GeneralizedNotationNotation/output' and site_output_file='/home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_pipeline_summary_site.html'\n2025-06-07 12:31:13,906 - src.site.generator - INFO - Starting HTML report generation for directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output\n2025-06-07 12:31:13,906 - src.site.generator - INFO - Output HTML will be saved to: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_pipeline_summary_site.html\n2025-06-07 12:31:13,906 - src.site.generator - WARNING - Pipeline summary JSON not found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/pipeline_execution_summary.json\n2025-06-07 12:31:13,942 - src.site.generator - WARNING - Pytest XML report not found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/test_reports/pytest_report.xml\n2025-06-07 12:31:14,001 - src.site.generator - WARNING - PyMDP Execute Logs directory not found: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/pymdp_execute_logs\n2025-06-07 12:31:14,003 - src.site.generator - INFO - \u2705 HTML report generated successfully: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_pipeline_summary_site.html\n2025-06-07 12:31:14,003 - __main__ - INFO - HTML summary site generated successfully: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/gnn_pipeline_summary_site.html\n",
"stderr": ""
}
],
"end_time": "2025-06-07T12:31:14.017500",
"overall_status": "FAILED"
} GNN Discovery (Step 1)
GNN File Discovery Report
Summary
- Total .md files found: 2
- Files with 'ModelName' found: 2
- Files with 'StateSpaceBlock' found: 2
- Files with 'Connections' found: 2
- Files with processing errors: 0
Detailed File Analysis
src/gnn/examples/pymdp_pomdp_agent.md
- ModelName: Multifactor PyMDP Agent v1
- StateSpaceBlock: Found
- Connections: Found
- ModelParameters:
num_hidden_states_factors:[2, 3]num_obs_modalities:[3, 3, 3]num_control_factors:[1, 3]
src/gnn/examples/rxinfer_multiagent_gnn.md
- ModelName: Multi-agent Trajectory Planning
- StateSpaceBlock: Found
- Connections: Found
GNN Type Checker (Step 4)
Type Check Report
GNN Type Checker Report
pymdp_pomdp_agent.md: ✅ VALID
Path: src/gnn/examples/pymdp_pomdp_agent.md
rxinfer_multiagent_gnn.md: ✅ VALID
Path: src/gnn/examples/rxinfer_multiagent_gnn.md
Checked 2 files, 2 valid, 0 invalid
Resource Estimates: resource_estimates
Images
inference_time_html.png
memory_usage_html.png
storage_requirements_html.png
Markdown Reports
resource_report.md
GNN Resource Estimation Report
Analyzed 2 files Average Memory Usage: 0.50 KB Average Inference Time: 218.62 units Average Storage: 5.29 KB
pymdp_pomdp_agent.md
Path: src/gnn/examples/pymdp_pomdp_agent.md Memory Estimate: 0.48 KB Inference Estimate: 154.07 units Storage Estimate: 3.83 KB
Model Info
- variables_count: 21
- edges_count: 2
- time_spec: Dynamic
- equation_count: 5
Complexity Metrics
- state_space_complexity: 6.9658
- graph_density: 0.0048
- avg_in_degree: 1.0000
- avg_out_degree: 1.0000
- max_in_degree: 1.0000
- max_out_degree: 1.0000
- cyclic_complexity: 0.0000
- temporal_complexity: 0.0000
- equation_complexity: 8.7600
- overall_complexity: 8.7413
- variable_count: 21.0000
- edge_count: 2.0000
- total_state_space_dim: 124.0000
- max_variable_dim: 27.0000
rxinfer_multiagent_gnn.md
Path: src/gnn/examples/rxinfer_multiagent_gnn.md Memory Estimate: 0.52 KB Inference Estimate: 283.16 units Storage Estimate: 6.76 KB
Model Info
- variables_count: 60
- edges_count: 1
- time_spec: Dynamic
- equation_count: 15
Complexity Metrics
- state_space_complexity: 6.8202
- graph_density: 0.0003
- avg_in_degree: 1.0000
- avg_out_degree: 1.0000
- max_in_degree: 1.0000
- max_out_degree: 1.0000
- cyclic_complexity: 0.0000
- temporal_complexity: 0.0000
- equation_complexity: 3.2578
- overall_complexity: 5.3649
- variable_count: 60.0000
- edge_count: 1.0000
- total_state_space_dim: 112.0000
- max_variable_dim: 16.0000
Metric Definitions
General Metrics
- Memory Estimate (KB): Estimated RAM required to hold the model's variables and data structures in memory. Calculated based on variable dimensions and data types (e.g., float: 4 bytes, int: 4 bytes).
- Inference Estimate (units): A relative, abstract measure of computational cost for a single inference pass. It is derived from factors like model type (Static, Dynamic, Hierarchical), the number and type of variables, the complexity of connections (edges), and the operations defined in equations. Higher values indicate a more computationally intensive model. These units are not tied to a specific hardware time (e.g., milliseconds) but allow for comparison between different GNN models.
- Storage Estimate (KB): Estimated disk space required to store the model file. This includes the memory footprint of the data plus overhead for the GNN textual representation, metadata, comments, and equations.
Complexity Metrics (scores are generally relative; higher often means more complex)
- state_space_complexity: Logarithmic measure of the total dimensionality of all variables (sum of the product of dimensions for each variable). Represents the model's theoretical information capacity or the size of its state space.
- graph_density: Ratio of actual edges to the maximum possible edges in the model graph. A value of 0 indicates no connections, while 1 would mean a fully connected graph. Measures how interconnected the variables are.
- avg_in_degree: Average number of incoming connections (edges) per variable.
- avg_out_degree: Average number of outgoing connections (edges) per variable.
- max_in_degree: Maximum number of incoming connections for any single variable in the model.
- max_out_degree: Maximum number of outgoing connections for any single variable in the model.
- cyclic_complexity: A score indicating the presence and extent of cyclic patterns or feedback loops in the graph. Approximated based on the ratio of edges to variables; higher values suggest more complex recurrent interactions.
- temporal_complexity: Proportion of edges that involve time dependencies (e.g., connecting a variable at time
tto one att+1). Indicates the degree to which the model's behavior depends on past states or sequences. - equation_complexity: A measure based on the average length, number, and types of mathematical operators (e.g., +, *, log, softmax) used in the model's equations. Higher values suggest more intricate mathematical relationships between variables.
- overall_complexity: A weighted composite score (typically scaled, e.g., 0-10) that combines state space size, graph structure (density, cyclicity), temporal aspects, and equation complexity to provide a single, holistic measure of the model's intricacy.
HTML Reports/Outputs
resource_report_detailed.html
View standalone: resource_report_detailed.html
JSON Files
resource_data.json
{
"/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/pymdp_pomdp_agent.md": {
"file": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/pymdp_pomdp_agent.md",
"model_name": "Multifactor PyMDP Agent v1",
"memory_estimate": 0.484375,
"inference_estimate": 154.06988264859797,
"storage_estimate": 3.82846875,
"flops_estimate": {
"total_flops": 1050.0,
"matrix_operations": 0,
"element_operations": 0,
"nonlinear_operations": 0
},
"inference_time_estimate": {
"cpu_time_seconds": 2.1e-08,
"cpu_time_ms": 2.1e-05,
"cpu_time_us": 0.020999999999999998
},
"batched_inference_estimate": {
"batch_1": {
"flops": 1050.0,
"time_seconds": 2.1e-08,
"throughput_per_second": 47619047.61904762
},
"batch_8": {
"flops": 6674.971489500035,
"time_seconds": 1.334994297900007e-07,
"throughput_per_second": 59925349.58826627
},
"batch_32": {
"flops": 25518.25782075925,
"time_seconds": 5.10365156415185e-07,
"throughput_per_second": 62700205.13306323
},
"batch_128": {
"flops": 99830.77636640746,
"time_seconds": 1.9966155273281492e-06,
"throughput_per_second": 64108486.710652955
},
"batch_512": {
"flops": 394234.3967437306,
"time_seconds": 7.884687934874611e-06,
"throughput_per_second": 64935987.85760216
}
},
"model_overhead": {
"compilation_ms": 79,
"optimization_ms": 240.5,
"memory_overhead_kb": 2.572265625
},
"complexity": {
"state_space_complexity": 6.965784284662087,
"graph_density": 0.004761904761904762,
"avg_in_degree": 1.0,
"avg_out_degree": 1.0,
"max_in_degree": 1,
"max_out_degree": 1,
"cyclic_complexity": 0,
"temporal_complexity": 0.0,
"equation_complexity": 8.76,
"overall_complexity": 8.741273094711996,
"variable_count": 21,
"edge_count": 2,
"total_state_space_dim": 124,
"max_variable_dim": 27
},
"model_info": {
"variables_count": 21,
"edges_count": 2,
"time_spec": "Dynamic",
"equation_count": 5
}
},
"/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md": {
"file": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md",
"model_name": "Multi-agent Trajectory Planning",
"memory_estimate": 0.5166015625,
"inference_estimate": 283.1611446514433,
"storage_estimate": 6.7573515625,
"flops_estimate": {
"total_flops": 20.0,
"matrix_operations": 0,
"element_operations": 8,
"nonlinear_operations": 0
},
"inference_time_estimate": {
"cpu_time_seconds": 4e-10,
"cpu_time_ms": 4.0000000000000003e-07,
"cpu_time_us": 0.0004
},
"batched_inference_estimate": {
"batch_1": {
"flops": 20.0,
"time_seconds": 4e-10,
"throughput_per_second": 2500000000.0
},
"batch_8": {
"flops": 127.14231408571496,
"time_seconds": 2.5428462817142993e-09,
"throughput_per_second": 3146080853.383979
},
"batch_32": {
"flops": 486.0620537287476,
"time_seconds": 9.721241074574952e-09,
"throughput_per_second": 3291760769.48582
},
"batch_128": {
"flops": 1901.5385974553803,
"time_seconds": 3.8030771949107605e-08,
"throughput_per_second": 3365695552.30928
},
"batch_512": {
"flops": 7509.226604642487,
"time_seconds": 1.5018453209284973e-07,
"throughput_per_second": 3409139362.5241137
}
},
"model_overhead": {
"compilation_ms": 206,
"optimization_ms": 1820.0,
"memory_overhead_kb": 5.423828125
},
"complexity": {
"state_space_complexity": 6.820178962415188,
"graph_density": 0.0002824858757062147,
"avg_in_degree": 1.0,
"avg_out_degree": 1.0,
"max_in_degree": 1,
"max_out_degree": 1,
"cyclic_complexity": 0,
"temporal_complexity": 0.0,
"equation_complexity": 3.2577777777777777,
"overall_complexity": 5.364897390812113,
"variable_count": 60,
"edge_count": 1,
"total_state_space_dim": 112,
"max_variable_dim": 16
},
"model_info": {
"variables_count": 60,
"edges_count": 1,
"time_spec": "Dynamic",
"equation_count": 15
}
}
}GNN Exports (Step 5)
Export Step Report
📤 GNN Export Step Summary
🗓️ Generated: 2025-06-07 12:28:52
⚙️ Configuration
- Source Directory for GNN files:
src/gnn/examples - Base Output for Exports:
output/gnn_exports - Recursive Search: ✅ Enabled
📊 Export Statistics
- GNN Files Found/Attempted: 2
- GNN Files with Successful Exports (all selected formats): 2
- GNN Files with At Least One Export Failure: 0
Exports for gnn_exports: gnn_exports
Markdown Reports
5_export_step_report.md
📤 GNN Export Step Summary
🗓️ Generated: 2025-06-07 12:35:09
⚙️ Configuration
- Source Directory for GNN files:
src/gnn/examples - Base Output for Exports:
output/gnn_exports/gnn_exports - Recursive Search: ✅ Enabled
📊 Export Statistics
- GNN Files Found/Attempted: 2
- GNN Files with Successful Exports (all selected formats): 2
- GNN Files with At Least One Export Failure: 0
Exports for pymdp_pomdp_agent: pymdp_pomdp_agent
JSON Files
pymdp_pomdp_agent.json
{
"file_path": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/pymdp_pomdp_agent.md",
"name": "Multifactor PyMDP Agent v1",
"raw_sections": {
"GNNSection": "MultifactorPyMDPAgent",
"GNNVersionAndFlags": "GNN v1",
"ModelName": "Multifactor PyMDP Agent v1",
"ModelAnnotation": "This model represents a PyMDP agent with multiple observation modalities and hidden state factors.\n- Observation modalities: \"state_observation\" (3 outcomes), \"reward\" (3 outcomes), \"decision_proprioceptive\" (3 outcomes)\n- Hidden state factors: \"reward_level\" (2 states), \"decision_state\" (3 states)\n- Control: \"decision_state\" factor is controllable with 3 possible actions.\nThe parameterization is derived from a PyMDP Python script example.",
"StateSpaceBlock": "# A_matrices are defined per modality: A_m[observation_outcomes, state_factor0_states, state_factor1_states]\nA_m0[3,2,3,type=float] # Likelihood for modality 0 (\"state_observation\")\nA_m1[3,2,3,type=float] # Likelihood for modality 1 (\"reward\")\nA_m2[3,2,3,type=float] # Likelihood for modality 2 (\"decision_proprioceptive\")\n\n# B_matrices are defined per hidden state factor: B_f[states_next, states_previous, actions]\nB_f0[2,2,1,type=float] # Transitions for factor 0 (\"reward_level\"), 1 implicit action (uncontrolled)\nB_f1[3,3,3,type=float] # Transitions for factor 1 (\"decision_state\"), 3 actions\n\n# C_vectors are defined per modality: C_m[observation_outcomes]\nC_m0[3,type=float] # Preferences for modality 0\nC_m1[3,type=float] # Preferences for modality 1\nC_m2[3,type=float] # Preferences for modality 2\n\n# D_vectors are defined per hidden state factor: D_f[states]\nD_f0[2,type=float] # Prior for factor 0\nD_f1[3,type=float] # Prior for factor 1\n\n# Hidden States\ns_f0[2,1,type=float] # Hidden state for factor 0 (\"reward_level\")\ns_f1[3,1,type=float] # Hidden state for factor 1 (\"decision_state\")\ns_prime_f0[2,1,type=float] # Next hidden state for factor 0\ns_prime_f1[3,1,type=float] # Next hidden state for factor 1\n\n# Observations\no_m0[3,1,type=float] # Observation for modality 0\no_m1[3,1,type=float] # Observation for modality 1\no_m2[3,1,type=float] # Observation for modality 2\n\n# Policy and Control\n\u03c0_f1[3,type=float] # Policy (distribution over actions) for controllable factor 1\nu_f1[1,type=int] # Action taken for controllable factor 1\nG[1,type=float] # Expected Free Energy (overall, or can be per policy)\nt[1,type=int] # Time step",
"Connections": "(D_f0,D_f1)-(s_f0,s_f1)\n(s_f0,s_f1)-(A_m0,A_m1,A_m2)\n(A_m0,A_m1,A_m2)-(o_m0,o_m1,o_m2)\n(s_f0,s_f1,u_f1)-(B_f0,B_f1) # u_f1 primarily affects B_f1; B_f0 is uncontrolled\n(B_f0,B_f1)-(s_prime_f0,s_prime_f1)\n(C_m0,C_m1,C_m2)>G\nG>\u03c0_f1\n\u03c0_f1-u_f1\nG=ExpectedFreeEnergy\nt=Time",
"InitialParameterization": "# A_m0: num_obs[0]=3, num_states[0]=2, num_states[1]=3. Format: A[obs_idx][state_f0_idx][state_f1_idx]\n# A[0][:, :, 0] = np.ones((3,2))/3\n# A[0][:, :, 1] = np.ones((3,2))/3\n# A[0][:, :, 2] = [[0.8,0.2],[0.0,0.0],[0.2,0.8]] (obs x state_f0 for state_f1=2)\nA_m0={\n ( (0.33333,0.33333,0.8), (0.33333,0.33333,0.2) ), # obs=0; (vals for s_f1 over s_f0=0), (vals for s_f1 over s_f0=1)\n ( (0.33333,0.33333,0.0), (0.33333,0.33333,0.0) ), # obs=1\n ( (0.33333,0.33333,0.2), (0.33333,0.33333,0.8) ) # obs=2\n}\n\n# A_m1: num_obs[1]=3, num_states[0]=2, num_states[1]=3\n# A[1][2, :, 0] = [1.0,1.0]\n# A[1][0:2, :, 1] = softmax([[1,0],[0,1]]) approx [[0.731,0.269],[0.269,0.731]]\n# A[1][2, :, 2] = [1.0,1.0]\n# Others are 0.\nA_m1={\n ( (0.0,0.731,0.0), (0.0,0.269,0.0) ), # obs=0\n ( (0.0,0.269,0.0), (0.0,0.731,0.0) ), # obs=1\n ( (1.0,0.0,1.0), (1.0,0.0,1.0) ) # obs=2\n}\n\n# A_m2: num_obs[2]=3, num_states[0]=2, num_states[1]=3\n# A[2][0,:,0]=1.0; A[2][1,:,1]=1.0; A[2][2,:,2]=1.0\n# Others are 0.\nA_m2={\n ( (1.0,0.0,0.0), (1.0,0.0,0.0) ), # obs=0\n ( (0.0,1.0,0.0), (0.0,1.0,0.0) ), # obs=1\n ( (0.0,0.0,1.0), (0.0,0.0,1.0) ) # obs=2\n}\n\n# B_f0: factor 0 (2 states), uncontrolled (1 action). Format B[s_next, s_prev, action=0]\n# B_f0 = eye(2)\nB_f0={\n ( (1.0),(0.0) ), # s_next=0; (vals for s_prev over action=0)\n ( (0.0),(1.0) ) # s_next=1\n}\n\n# B_f1: factor 1 (3 states), 3 actions. Format B[s_next, s_prev, action_idx]\n# B_f1[:,:,action_idx] = eye(3) for each action\nB_f1={\n ( (1.0,1.0,1.0), (0.0,0.0,0.0), (0.0,0.0,0.0) ), # s_next=0; (vals for actions over s_prev=0), (vals for actions over s_prev=1), ...\n ( (0.0,0.0,0.0), (1.0,1.0,1.0), (0.0,0.0,0.0) ), # s_next=1\n ( (0.0,0.0,0.0), (0.0,0.0,0.0), (1.0,1.0,1.0) ) # s_next=2\n}\n\n# C_m0: num_obs[0]=3. Defaults to zeros.\nC_m0={(0.0,0.0,0.0)}\n\n# C_m1: num_obs[1]=3. C[1][0]=1.0, C[1][1]=-2.0\nC_m1={(1.0,-2.0,0.0)}\n\n# C_m2: num_obs[2]=3. Defaults to zeros.\nC_m2={(0.0,0.0,0.0)}\n\n# D_f0: factor 0 (2 states). Uniform prior.\nD_f0={(0.5,0.5)}\n\n# D_f1: factor 1 (3 states). Uniform prior.\nD_f1={(0.33333,0.33333,0.33333)}",
"Equations": "# Standard PyMDP agent equations for state inference (infer_states),\n# policy inference (infer_policies), and action sampling (sample_action).\n# qs = infer_states(o)\n# q_pi, efe = infer_policies()\n# action = sample_action()",
"Time": "Dynamic\nDiscreteTime=t\nModelTimeHorizon=Unbounded # Agent definition is generally unbounded, specific simulation runs have a horizon.",
"ActInfOntologyAnnotation": "A_m0=LikelihoodMatrixModality0\nA_m1=LikelihoodMatrixModality1\nA_m2=LikelihoodMatrixModality2\nB_f0=TransitionMatrixFactor0\nB_f1=TransitionMatrixFactor1\nC_m0=LogPreferenceVectorModality0\nC_m1=LogPreferenceVectorModality1\nC_m2=LogPreferenceVectorModality2\nD_f0=PriorOverHiddenStatesFactor0\nD_f1=PriorOverHiddenStatesFactor1\ns_f0=HiddenStateFactor0\ns_f1=HiddenStateFactor1\ns_prime_f0=NextHiddenStateFactor0\ns_prime_f1=NextHiddenStateFactor1\no_m0=ObservationModality0\no_m1=ObservationModality1\no_m2=ObservationModality2\n\u03c0_f1=PolicyVectorFactor1 # Distribution over actions for factor 1\nu_f1=ActionFactor1 # Chosen action for factor 1\nG=ExpectedFreeEnergy",
"ModelParameters": "num_hidden_states_factors: [2, 3] # s_f0[2], s_f1[3]\nnum_obs_modalities: [3, 3, 3] # o_m0[3], o_m1[3], o_m2[3]\nnum_control_factors: [1, 3] # B_f0 actions_dim=1 (uncontrolled), B_f1 actions_dim=3 (controlled by pi_f1)",
"Footer": "Multifactor PyMDP Agent v1 - GNN Representation",
"Signature": "NA"
},
"statespaceblock": [
{
"id": "A_m0",
"dimensions": "3,2,3,type=float",
"original_id": "A_m0"
},
{
"id": "A_m1",
"dimensions": "3,2,3,type=float",
"original_id": "A_m1"
},
{
"id": "A_m2",
"dimensions": "3,2,3,type=float",
"original_id": "A_m2"
},
{
"id": "B_f0",
"dimensions": "2,2,1,type=float",
"original_id": "B_f0"
},
{
"id": "B_f1",
"dimensions": "3,3,3,type=float",
"original_id": "B_f1"
},
{
"id": "C_m0",
"dimensions": "3,type=float",
"original_id": "C_m0"
},
{
"id": "C_m1",
"dimensions": "3,type=float",
"original_id": "C_m1"
},
{
"id": "C_m2",
"dimensions": "3,type=float",
"original_id": "C_m2"
},
{
"id": "D_f0",
"dimensions": "2,type=float",
"original_id": "D_f0"
},
{
"id": "D_f1",
"dimensions": "3,type=float",
"original_id": "D_f1"
},
{
"id": "s_f0",
"dimensions": "2,1,type=float",
"original_id": "s_f0"
},
{
"id": "s_f1",
"dimensions": "3,1,type=float",
"original_id": "s_f1"
},
{
"id": "s_prime_f0",
"dimensions": "2,1,type=float",
"original_id": "s_prime_f0"
},
{
"id": "s_prime_f1",
"dimensions": "3,1,type=float",
"original_id": "s_prime_f1"
},
{
"id": "o_m0",
"dimensions": "3,1,type=float",
"original_id": "o_m0"
},
{
"id": "o_m1",
"dimensions": "3,1,type=float",
"original_id": "o_m1"
},
{
"id": "o_m2",
"dimensions": "3,1,type=float",
"original_id": "o_m2"
},
{
"id": "u_f1",
"dimensions": "1,type=int",
"original_id": "u_f1"
},
{
"id": "G",
"dimensions": "1,type=float",
"original_id": "G"
},
{
"id": "t",
"dimensions": "1,type=int",
"original_id": "t"
}
],
"connections": [
{
"sources": [
"D_f0",
"D_f1"
],
"operator": "-",
"targets": [
"s_f0",
"s_f1"
],
"attributes": {}
},
{
"sources": [
"s_f0",
"s_f1"
],
"operator": "-",
"targets": [
"A_m0",
"A_m1",
"A_m2"
],
"attributes": {}
},
{
"sources": [
"A_m0",
"A_m1",
"A_m2"
],
"operator": "-",
"targets": [
"o_m0",
"o_m1",
"o_m2"
],
"attributes": {}
},
{
"sources": [
"s_f0",
"s_f1",
"u_f1"
],
"operator": "-",
"targets": [
"B_f0",
"B_f1"
],
"attributes": {}
},
{
"sources": [
"B_f0",
"B_f1"
],
"operator": "-",
"targets": [
"s_prime_f0",
"s_prime_f1"
],
"attributes": {}
},
{
"sources": [
"C_m0",
"C_m1",
"C_m2"
],
"operator": ">",
"targets": [
"G"
],
"attributes": {}
},
{
"sources": [
"G"
],
"operator": ">",
"targets": [
"\u03c0_f1"
],
"attributes": {}
},
{
"sources": [
"\u03c0_f1"
],
"operator": "-",
"targets": [
"u_f1"
],
"attributes": {}
}
],
"initialparameterization": {
"A_m0": [
[
[
0.33333,
0.33333,
0.8
],
[
0.33333,
0.33333,
0.2
]
],
[
[
0.33333,
0.33333,
0.0
],
[
0.33333,
0.33333,
0.0
]
],
[
[
0.33333,
0.33333,
0.2
],
[
0.33333,
0.33333,
0.8
]
]
],
"A_m1": [
[
[
0.0,
0.731,
0.0
],
[
0.0,
0.269,
0.0
]
],
[
[
0.0,
0.269,
0.0
],
[
0.0,
0.731,
0.0
]
],
[
[
1.0,
0.0,
1.0
],
[
1.0,
0.0,
1.0
]
]
],
"A_m2": [
[
[
1.0,
0.0,
0.0
],
[
1.0,
0.0,
0.0
]
],
[
[
0.0,
1.0,
0.0
],
[
0.0,
1.0,
0.0
]
],
[
[
0.0,
0.0,
1.0
],
[
0.0,
0.0,
1.0
]
]
],
"B_f0": [
[
1.0,
0.0
],
[
0.0,
1.0
]
],
"B_f1": [
[
[
1.0,
1.0,
1.0
],
[
0.0,
0.0,
0.0
],
[
0.0,
0.0,
0.0
]
],
[
[
0.0,
0.0,
0.0
],
[
1.0,
1.0,
1.0
],
[
0.0,
0.0,
0.0
]
],
[
[
0.0,
0.0,
0.0
],
[
0.0,
0.0,
0.0
],
[
1.0,
1.0,
1.0
]
]
],
"C_m0": [
0.0,
0.0,
0.0
],
"C_m1": [
1.0,
-2.0,
0.0
],
"C_m2": [
0.0,
0.0,
0.0
],
"D_f0": [
0.5,
0.5
],
"D_f1": [
0.33333,
0.33333,
0.33333
]
}
}Text/Log Files
pymdp_pomdp_agent.txt
GNN Model Summary: Multifactor PyMDP Agent v1
--- STATESPACEBLOCK ---
- {'id': 'A_m0', 'dimensions': '3,2,3,type=float', 'original_id': 'A_m0'}
- {'id': 'A_m1', 'dimensions': '3,2,3,type=float', 'original_id': 'A_m1'}
- {'id': 'A_m2', 'dimensions': '3,2,3,type=float', 'original_id': 'A_m2'}
- {'id': 'B_f0', 'dimensions': '2,2,1,type=float', 'original_id': 'B_f0'}
- {'id': 'B_f1', 'dimensions': '3,3,3,type=float', 'original_id': 'B_f1'}
- {'id': 'C_m0', 'dimensions': '3,type=float', 'original_id': 'C_m0'}
- {'id': 'C_m1', 'dimensions': '3,type=float', 'original_id': 'C_m1'}
- {'id': 'C_m2', 'dimensions': '3,type=float', 'original_id': 'C_m2'}
- {'id': 'D_f0', 'dimensions': '2,type=float', 'original_id': 'D_f0'}
- {'id': 'D_f1', 'dimensions': '3,type=float', 'original_id': 'D_f1'}
- {'id': 's_f0', 'dimensions': '2,1,type=float', 'original_id': 's_f0'}
- {'id': 's_f1', 'dimensions': '3,1,type=float', 'original_id': 's_f1'}
- {'id': 's_prime_f0', 'dimensions': '2,1,type=float', 'original_id': 's_prime_f0'}
- {'id': 's_prime_f1', 'dimensions': '3,1,type=float', 'original_id': 's_prime_f1'}
- {'id': 'o_m0', 'dimensions': '3,1,type=float', 'original_id': 'o_m0'}
- {'id': 'o_m1', 'dimensions': '3,1,type=float', 'original_id': 'o_m1'}
- {'id': 'o_m2', 'dimensions': '3,1,type=float', 'original_id': 'o_m2'}
- {'id': 'u_f1', 'dimensions': '1,type=int', 'original_id': 'u_f1'}
- {'id': 'G', 'dimensions': '1,type=float', 'original_id': 'G'}
- {'id': 't', 'dimensions': '1,type=int', 'original_id': 't'}
--- CONNECTIONS ---
- {'sources': ['D_f0', 'D_f1'], 'operator': '-', 'targets': ['s_f0', 's_f1'], 'attributes': {}}
- {'sources': ['s_f0', 's_f1'], 'operator': '-', 'targets': ['A_m0', 'A_m1', 'A_m2'], 'attributes': {}}
- {'sources': ['A_m0', 'A_m1', 'A_m2'], 'operator': '-', 'targets': ['o_m0', 'o_m1', 'o_m2'], 'attributes': {}}
- {'sources': ['s_f0', 's_f1', 'u_f1'], 'operator': '-', 'targets': ['B_f0', 'B_f1'], 'attributes': {}}
- {'sources': ['B_f0', 'B_f1'], 'operator': '-', 'targets': ['s_prime_f0', 's_prime_f1'], 'attributes': {}}
- {'sources': ['C_m0', 'C_m1', 'C_m2'], 'operator': '>', 'targets': ['G'], 'attributes': {}}
- {'sources': ['G'], 'operator': '>', 'targets': ['π_f1'], 'attributes': {}}
- {'sources': ['π_f1'], 'operator': '-', 'targets': ['u_f1'], 'attributes': {}}
--- INITIALPARAMETERIZATION ---
- A_m0: [((0.33333, 0.33333, 0.8), (0.33333, 0.33333, 0.2)), ((0.33333, 0.33333, 0.0), (0.33333, 0.33333, 0.0)), ((0.33333, 0.33333, 0.2), (0.33333, 0.33333, 0.8))]
- A_m1: [((0.0, 0.731, 0.0), (0.0, 0.269, 0.0)), ((0.0, 0.269, 0.0), (0.0, 0.731, 0.0)), ((1.0, 0.0, 1.0), (1.0, 0.0, 1.0))]
- A_m2: [((1.0, 0.0, 0.0), (1.0, 0.0, 0.0)), ((0.0, 1.0, 0.0), (0.0, 1.0, 0.0)), ((0.0, 0.0, 1.0), (0.0, 0.0, 1.0))]
- B_f0: [(1.0, 0.0), (0.0, 1.0)]
- B_f1: [((1.0, 1.0, 1.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0)), ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0), (0.0, 0.0, 0.0)), ((0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (1.0, 1.0, 1.0))]
- C_m0: [0.0, 0.0, 0.0]
- C_m1: [1.0, -2.0, 0.0]
- C_m2: [0.0, 0.0, 0.0]
- D_f0: [0.5, 0.5]
- D_f1: [0.33333, 0.33333, 0.33333]
Other Files
Exports for rxinfer_multiagent_gnn: rxinfer_multiagent_gnn
JSON Files
rxinfer_multiagent_gnn.json
{
"file_path": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md",
"name": "Multi-agent Trajectory Planning",
"raw_sections": {
"GNNSection": "RxInferMultiAgentTrajectoryPlanning",
"GNNVersionAndFlags": "GNN v1",
"ModelName": "Multi-agent Trajectory Planning",
"ModelAnnotation": "This model represents a multi-agent trajectory planning scenario in RxInfer.jl.\nIt includes:\n- State space model for agents moving in a 2D environment\n- Obstacle avoidance constraints\n- Goal-directed behavior\n- Inter-agent collision avoidance\nThe model can be used to simulate trajectory planning in various environments with obstacles.",
"StateSpaceBlock": "# Model parameters\ndt[1,type=float] # Time step for the state space model\ngamma[1,type=float] # Constraint parameter for the Halfspace node\nnr_steps[1,type=int] # Number of time steps in the trajectory\nnr_iterations[1,type=int] # Number of inference iterations\nnr_agents[1,type=int] # Number of agents in the simulation\nsoftmin_temperature[1,type=float] # Temperature parameter for the softmin function\nintermediate_steps[1,type=int] # Intermediate results saving interval\nsave_intermediates[1,type=bool] # Whether to save intermediate results\n\n# State space matrices\nA[4,4,type=float] # State transition matrix\nB[4,2,type=float] # Control input matrix\nC[2,4,type=float] # Observation matrix\n\n# Prior distributions\ninitial_state_variance[1,type=float] # Prior on initial state\ncontrol_variance[1,type=float] # Prior on control inputs\ngoal_constraint_variance[1,type=float] # Goal constraints variance\ngamma_shape[1,type=float] # Parameters for GammaShapeRate prior\ngamma_scale_factor[1,type=float] # Parameters for GammaShapeRate prior\n\n# Visualization parameters\nx_limits[2,type=float] # Plot boundaries (x-axis)\ny_limits[2,type=float] # Plot boundaries (y-axis)\nfps[1,type=int] # Animation frames per second\nheatmap_resolution[1,type=int] # Heatmap resolution\nplot_width[1,type=int] # Plot width\nplot_height[1,type=int] # Plot height\nagent_alpha[1,type=float] # Visualization alpha for agents\ntarget_alpha[1,type=float] # Visualization alpha for targets\ncolor_palette[1,type=string] # Color palette for visualization\n\n# Environment definitions\ndoor_obstacle_center_1[2,type=float] # Door environment, obstacle 1 center\ndoor_obstacle_size_1[2,type=float] # Door environment, obstacle 1 size\ndoor_obstacle_center_2[2,type=float] # Door environment, obstacle 2 center\ndoor_obstacle_size_2[2,type=float] # Door environment, obstacle 2 size\n\nwall_obstacle_center[2,type=float] # Wall environment, obstacle center\nwall_obstacle_size[2,type=float] # Wall environment, obstacle size\n\ncombined_obstacle_center_1[2,type=float] # Combined environment, obstacle 1 center\ncombined_obstacle_size_1[2,type=float] # Combined environment, obstacle 1 size\ncombined_obstacle_center_2[2,type=float] # Combined environment, obstacle 2 center\ncombined_obstacle_size_2[2,type=float] # Combined environment, obstacle 2 size\ncombined_obstacle_center_3[2,type=float] # Combined environment, obstacle 3 center\ncombined_obstacle_size_3[2,type=float] # Combined environment, obstacle 3 size\n\n# Agent configurations\nagent1_id[1,type=int] # Agent 1 ID\nagent1_radius[1,type=float] # Agent 1 radius\nagent1_initial_position[2,type=float] # Agent 1 initial position\nagent1_target_position[2,type=float] # Agent 1 target position\n\nagent2_id[1,type=int] # Agent 2 ID\nagent2_radius[1,type=float] # Agent 2 radius\nagent2_initial_position[2,type=float] # Agent 2 initial position\nagent2_target_position[2,type=float] # Agent 2 target position\n\nagent3_id[1,type=int] # Agent 3 ID\nagent3_radius[1,type=float] # Agent 3 radius\nagent3_initial_position[2,type=float] # Agent 3 initial position\nagent3_target_position[2,type=float] # Agent 3 target position\n\nagent4_id[1,type=int] # Agent 4 ID\nagent4_radius[1,type=float] # Agent 4 radius\nagent4_initial_position[2,type=float] # Agent 4 initial position\nagent4_target_position[2,type=float] # Agent 4 target position\n\n# Experiment configurations\nexperiment_seeds[2,type=int] # Random seeds for reproducibility\nresults_dir[1,type=string] # Base directory for results\nanimation_template[1,type=string] # Filename template for animations\ncontrol_vis_filename[1,type=string] # Filename for control visualization\nobstacle_distance_filename[1,type=string] # Filename for obstacle distance plot\npath_uncertainty_filename[1,type=string] # Filename for path uncertainty plot\nconvergence_filename[1,type=string] # Filename for convergence plot",
"Connections": "# Model parameters\ndt > A\n(A, B, C) > state_space_model\n\n# Agent trajectories\n(state_space_model, initial_state_variance, control_variance) > agent_trajectories\n\n# Goal constraints\n(agent_trajectories, goal_constraint_variance) > goal_directed_behavior\n\n# Obstacle avoidance\n(agent_trajectories, gamma, gamma_shape, gamma_scale_factor) > obstacle_avoidance\n\n# Collision avoidance\n(agent_trajectories, nr_agents) > collision_avoidance\n\n# Complete planning system\n(goal_directed_behavior, obstacle_avoidance, collision_avoidance) > planning_system",
"InitialParameterization": "# Model parameters\ndt=1.0\ngamma=1.0\nnr_steps=40\nnr_iterations=350\nnr_agents=4\nsoftmin_temperature=10.0\nintermediate_steps=10\nsave_intermediates=false\n\n# State space matrices\n# A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1]\nA={(1.0, 1.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.0, 0.0, 1.0)}\n\n# B = [0 0; dt 0; 0 0; 0 dt]\nB={(0.0, 0.0), (1.0, 0.0), (0.0, 0.0), (0.0, 1.0)}\n\n# C = [1 0 0 0; 0 0 1 0]\nC={(1.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)}\n\n# Prior distributions\ninitial_state_variance=100.0\ncontrol_variance=0.1\ngoal_constraint_variance=0.00001\ngamma_shape=1.5\ngamma_scale_factor=0.5\n\n# Visualization parameters\nx_limits={(-20, 20)}\ny_limits={(-20, 20)}\nfps=15\nheatmap_resolution=100\nplot_width=800\nplot_height=400\nagent_alpha=1.0\ntarget_alpha=0.2\ncolor_palette=\"tab10\"\n\n# Environment definitions\ndoor_obstacle_center_1={(-40.0, 0.0)}\ndoor_obstacle_size_1={(70.0, 5.0)}\ndoor_obstacle_center_2={(40.0, 0.0)}\ndoor_obstacle_size_2={(70.0, 5.0)}\n\nwall_obstacle_center={(0.0, 0.0)}\nwall_obstacle_size={(10.0, 5.0)}\n\ncombined_obstacle_center_1={(-50.0, 0.0)}\ncombined_obstacle_size_1={(70.0, 2.0)}\ncombined_obstacle_center_2={(50.0, 0.0)}\ncombined_obstacle_size_2={(70.0, 2.0)}\ncombined_obstacle_center_3={(5.0, -1.0)}\ncombined_obstacle_size_3={(3.0, 10.0)}\n\n# Agent configurations\nagent1_id=1\nagent1_radius=2.5\nagent1_initial_position={(-4.0, 10.0)}\nagent1_target_position={(-10.0, -10.0)}\n\nagent2_id=2\nagent2_radius=1.5\nagent2_initial_position={(-10.0, 5.0)}\nagent2_target_position={(10.0, -15.0)}\n\nagent3_id=3\nagent3_radius=1.0\nagent3_initial_position={(-15.0, -10.0)}\nagent3_target_position={(10.0, 10.0)}\n\nagent4_id=4\nagent4_radius=2.5\nagent4_initial_position={(0.0, -10.0)}\nagent4_target_position={(-10.0, 15.0)}\n\n# Experiment configurations\nexperiment_seeds={(42, 123)}\nresults_dir=\"results\"\nanimation_template=\"{environment}_{seed}.gif\"\ncontrol_vis_filename=\"control_signals.gif\"\nobstacle_distance_filename=\"obstacle_distance.png\"\npath_uncertainty_filename=\"path_uncertainty.png\"\nconvergence_filename=\"convergence.png\"",
"Equations": "# State space model:\n# x_{t+1} = A * x_t + B * u_t + w_t, w_t ~ N(0, control_variance)\n# y_t = C * x_t + v_t, v_t ~ N(0, observation_variance)\n#\n# Obstacle avoidance constraint:\n# p(x_t | obstacle) ~ N(d(x_t, obstacle), gamma)\n# where d(x_t, obstacle) is the distance from position x_t to the nearest obstacle\n#\n# Goal constraint:\n# p(x_T | goal) ~ N(goal, goal_constraint_variance)\n# where x_T is the final position\n#\n# Collision avoidance constraint:\n# p(x_i, x_j) ~ N(||x_i - x_j|| - (r_i + r_j), gamma)\n# where x_i, x_j are positions of agents i and j, r_i, r_j are their radii",
"Time": "Dynamic\nDiscreteTime\nModelTimeHorizon=nr_steps",
"ActInfOntologyAnnotation": "dt=TimeStep\ngamma=ConstraintParameter\nnr_steps=TrajectoryLength\nnr_iterations=InferenceIterations\nnr_agents=NumberOfAgents\nsoftmin_temperature=SoftminTemperature\nA=StateTransitionMatrix\nB=ControlInputMatrix\nC=ObservationMatrix\ninitial_state_variance=InitialStateVariance\ncontrol_variance=ControlVariance\ngoal_constraint_variance=GoalConstraintVariance",
"ModelParameters": "nr_agents=4\nnr_steps=40\nnr_iterations=350",
"Footer": "Multi-agent Trajectory Planning - GNN Representation for RxInfer.jl",
"Signature": "Creator: AI Assistant for GNN\nDate: 2024-07-27\nStatus: Example for RxInfer.jl multi-agent trajectory planning"
},
"statespaceblock": [
{
"id": "dt",
"dimensions": "1,type=float",
"original_id": "dt"
},
{
"id": "gamma",
"dimensions": "1,type=float",
"original_id": "gamma"
},
{
"id": "nr_steps",
"dimensions": "1,type=int",
"original_id": "nr_steps"
},
{
"id": "nr_iterations",
"dimensions": "1,type=int",
"original_id": "nr_iterations"
},
{
"id": "nr_agents",
"dimensions": "1,type=int",
"original_id": "nr_agents"
},
{
"id": "softmin_temperature",
"dimensions": "1,type=float",
"original_id": "softmin_temperature"
},
{
"id": "intermediate_steps",
"dimensions": "1,type=int",
"original_id": "intermediate_steps"
},
{
"id": "save_intermediates",
"dimensions": "1,type=bool",
"original_id": "save_intermediates"
},
{
"id": "A",
"dimensions": "4,4,type=float",
"original_id": "A"
},
{
"id": "B",
"dimensions": "4,2,type=float",
"original_id": "B"
},
{
"id": "C",
"dimensions": "2,4,type=float",
"original_id": "C"
},
{
"id": "initial_state_variance",
"dimensions": "1,type=float",
"original_id": "initial_state_variance"
},
{
"id": "control_variance",
"dimensions": "1,type=float",
"original_id": "control_variance"
},
{
"id": "goal_constraint_variance",
"dimensions": "1,type=float",
"original_id": "goal_constraint_variance"
},
{
"id": "gamma_shape",
"dimensions": "1,type=float",
"original_id": "gamma_shape"
},
{
"id": "gamma_scale_factor",
"dimensions": "1,type=float",
"original_id": "gamma_scale_factor"
},
{
"id": "x_limits",
"dimensions": "2,type=float",
"original_id": "x_limits"
},
{
"id": "y_limits",
"dimensions": "2,type=float",
"original_id": "y_limits"
},
{
"id": "fps",
"dimensions": "1,type=int",
"original_id": "fps"
},
{
"id": "heatmap_resolution",
"dimensions": "1,type=int",
"original_id": "heatmap_resolution"
},
{
"id": "plot_width",
"dimensions": "1,type=int",
"original_id": "plot_width"
},
{
"id": "plot_height",
"dimensions": "1,type=int",
"original_id": "plot_height"
},
{
"id": "agent_alpha",
"dimensions": "1,type=float",
"original_id": "agent_alpha"
},
{
"id": "target_alpha",
"dimensions": "1,type=float",
"original_id": "target_alpha"
},
{
"id": "color_palette",
"dimensions": "1,type=string",
"original_id": "color_palette"
},
{
"id": "door_obstacle_center_1",
"dimensions": "2,type=float",
"original_id": "door_obstacle_center_1"
},
{
"id": "door_obstacle_size_1",
"dimensions": "2,type=float",
"original_id": "door_obstacle_size_1"
},
{
"id": "door_obstacle_center_2",
"dimensions": "2,type=float",
"original_id": "door_obstacle_center_2"
},
{
"id": "door_obstacle_size_2",
"dimensions": "2,type=float",
"original_id": "door_obstacle_size_2"
},
{
"id": "wall_obstacle_center",
"dimensions": "2,type=float",
"original_id": "wall_obstacle_center"
},
{
"id": "wall_obstacle_size",
"dimensions": "2,type=float",
"original_id": "wall_obstacle_size"
},
{
"id": "combined_obstacle_center_1",
"dimensions": "2,type=float",
"original_id": "combined_obstacle_center_1"
},
{
"id": "combined_obstacle_size_1",
"dimensions": "2,type=float",
"original_id": "combined_obstacle_size_1"
},
{
"id": "combined_obstacle_center_2",
"dimensions": "2,type=float",
"original_id": "combined_obstacle_center_2"
},
{
"id": "combined_obstacle_size_2",
"dimensions": "2,type=float",
"original_id": "combined_obstacle_size_2"
},
{
"id": "combined_obstacle_center_3",
"dimensions": "2,type=float",
"original_id": "combined_obstacle_center_3"
},
{
"id": "combined_obstacle_size_3",
"dimensions": "2,type=float",
"original_id": "combined_obstacle_size_3"
},
{
"id": "agent1_id",
"dimensions": "1,type=int",
"original_id": "agent1_id"
},
{
"id": "agent1_radius",
"dimensions": "1,type=float",
"original_id": "agent1_radius"
},
{
"id": "agent1_initial_position",
"dimensions": "2,type=float",
"original_id": "agent1_initial_position"
},
{
"id": "agent1_target_position",
"dimensions": "2,type=float",
"original_id": "agent1_target_position"
},
{
"id": "agent2_id",
"dimensions": "1,type=int",
"original_id": "agent2_id"
},
{
"id": "agent2_radius",
"dimensions": "1,type=float",
"original_id": "agent2_radius"
},
{
"id": "agent2_initial_position",
"dimensions": "2,type=float",
"original_id": "agent2_initial_position"
},
{
"id": "agent2_target_position",
"dimensions": "2,type=float",
"original_id": "agent2_target_position"
},
{
"id": "agent3_id",
"dimensions": "1,type=int",
"original_id": "agent3_id"
},
{
"id": "agent3_radius",
"dimensions": "1,type=float",
"original_id": "agent3_radius"
},
{
"id": "agent3_initial_position",
"dimensions": "2,type=float",
"original_id": "agent3_initial_position"
},
{
"id": "agent3_target_position",
"dimensions": "2,type=float",
"original_id": "agent3_target_position"
},
{
"id": "agent4_id",
"dimensions": "1,type=int",
"original_id": "agent4_id"
},
{
"id": "agent4_radius",
"dimensions": "1,type=float",
"original_id": "agent4_radius"
},
{
"id": "agent4_initial_position",
"dimensions": "2,type=float",
"original_id": "agent4_initial_position"
},
{
"id": "agent4_target_position",
"dimensions": "2,type=float",
"original_id": "agent4_target_position"
},
{
"id": "experiment_seeds",
"dimensions": "2,type=int",
"original_id": "experiment_seeds"
},
{
"id": "results_dir",
"dimensions": "1,type=string",
"original_id": "results_dir"
},
{
"id": "animation_template",
"dimensions": "1,type=string",
"original_id": "animation_template"
},
{
"id": "control_vis_filename",
"dimensions": "1,type=string",
"original_id": "control_vis_filename"
},
{
"id": "obstacle_distance_filename",
"dimensions": "1,type=string",
"original_id": "obstacle_distance_filename"
},
{
"id": "path_uncertainty_filename",
"dimensions": "1,type=string",
"original_id": "path_uncertainty_filename"
},
{
"id": "convergence_filename",
"dimensions": "1,type=string",
"original_id": "convergence_filename"
}
],
"connections": [
{
"sources": [
"dt"
],
"operator": ">",
"targets": [
"A"
],
"attributes": {}
},
{
"sources": [
"A",
"B",
"C"
],
"operator": ">",
"targets": [
"state_space_model"
],
"attributes": {}
},
{
"sources": [
"state_space_model",
"initial_state_variance",
"control_variance"
],
"operator": ">",
"targets": [
"agent_trajectories"
],
"attributes": {}
},
{
"sources": [
"agent_trajectories",
"goal_constraint_variance"
],
"operator": ">",
"targets": [
"goal_directed_behavior"
],
"attributes": {}
},
{
"sources": [
"agent_trajectories",
"gamma",
"gamma_shape",
"gamma_scale_factor"
],
"operator": ">",
"targets": [
"obstacle_avoidance"
],
"attributes": {}
},
{
"sources": [
"agent_trajectories",
"nr_agents"
],
"operator": ">",
"targets": [
"collision_avoidance"
],
"attributes": {}
},
{
"sources": [
"goal_directed_behavior",
"obstacle_avoidance",
"collision_avoidance"
],
"operator": ">",
"targets": [
"planning_system"
],
"attributes": {}
}
],
"initialparameterization": {
"dt": 1.0,
"gamma": 1.0,
"nr_steps": 40,
"nr_iterations": 350,
"nr_agents": 4,
"softmin_temperature": 10.0,
"intermediate_steps": 10,
"save_intermediates": "false\n\n# State space matrices\n# A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1]",
"A": [
[
1.0,
1.0,
0.0,
0.0
],
[
0.0,
1.0,
0.0,
0.0
],
[
0.0,
0.0,
1.0,
1.0
],
[
0.0,
0.0,
0.0,
1.0
]
],
"B": [
[
0.0,
0.0
],
[
1.0,
0.0
],
[
0.0,
0.0
],
[
0.0,
1.0
]
],
"C": [
[
1.0,
0.0,
0.0,
0.0
],
[
0.0,
0.0,
1.0,
0.0
]
],
"initial_state_variance": 100.0,
"control_variance": 0.1,
"goal_constraint_variance": 1e-05,
"gamma_shape": 1.5,
"gamma_scale_factor": 0.5,
"x_limits": [
-20,
20
],
"y_limits": [
-20,
20
],
"fps": 15,
"heatmap_resolution": 100,
"plot_width": 800,
"plot_height": 400,
"agent_alpha": 1.0,
"target_alpha": 0.2,
"color_palette": "tab10",
"door_obstacle_center_1": [
-40.0,
0.0
],
"door_obstacle_size_1": [
70.0,
5.0
],
"door_obstacle_center_2": [
40.0,
0.0
],
"door_obstacle_size_2": [
70.0,
5.0
],
"wall_obstacle_center": [
0.0,
0.0
],
"wall_obstacle_size": [
10.0,
5.0
],
"combined_obstacle_center_1": [
-50.0,
0.0
],
"combined_obstacle_size_1": [
70.0,
2.0
],
"combined_obstacle_center_2": [
50.0,
0.0
],
"combined_obstacle_size_2": [
70.0,
2.0
],
"combined_obstacle_center_3": [
5.0,
-1.0
],
"combined_obstacle_size_3": [
3.0,
10.0
],
"agent1_id": 1,
"agent1_radius": 2.5,
"agent1_initial_position": [
-4.0,
10.0
],
"agent1_target_position": [
-10.0,
-10.0
],
"agent2_id": 2,
"agent2_radius": 1.5,
"agent2_initial_position": [
-10.0,
5.0
],
"agent2_target_position": [
10.0,
-15.0
],
"agent3_id": 3,
"agent3_radius": 1.0,
"agent3_initial_position": [
-15.0,
-10.0
],
"agent3_target_position": [
10.0,
10.0
],
"agent4_id": 4,
"agent4_radius": 2.5,
"agent4_initial_position": [
0.0,
-10.0
],
"agent4_target_position": [
-10.0,
15.0
],
"experiment_seeds": [
42,
123
],
"results_dir": "results",
"animation_template": "[environment]_[seed].gif",
"control_vis_filename": "control_signals.gif",
"obstacle_distance_filename": "obstacle_distance.png",
"path_uncertainty_filename": "path_uncertainty.png",
"convergence_filename": "convergence.png"
}
}Text/Log Files
rxinfer_multiagent_gnn.txt
GNN Model Summary: Multi-agent Trajectory Planning
--- STATESPACEBLOCK ---
- {'id': 'dt', 'dimensions': '1,type=float', 'original_id': 'dt'}
- {'id': 'gamma', 'dimensions': '1,type=float', 'original_id': 'gamma'}
- {'id': 'nr_steps', 'dimensions': '1,type=int', 'original_id': 'nr_steps'}
- {'id': 'nr_iterations', 'dimensions': '1,type=int', 'original_id': 'nr_iterations'}
- {'id': 'nr_agents', 'dimensions': '1,type=int', 'original_id': 'nr_agents'}
- {'id': 'softmin_temperature', 'dimensions': '1,type=float', 'original_id': 'softmin_temperature'}
- {'id': 'intermediate_steps', 'dimensions': '1,type=int', 'original_id': 'intermediate_steps'}
- {'id': 'save_intermediates', 'dimensions': '1,type=bool', 'original_id': 'save_intermediates'}
- {'id': 'A', 'dimensions': '4,4,type=float', 'original_id': 'A'}
- {'id': 'B', 'dimensions': '4,2,type=float', 'original_id': 'B'}
- {'id': 'C', 'dimensions': '2,4,type=float', 'original_id': 'C'}
- {'id': 'initial_state_variance', 'dimensions': '1,type=float', 'original_id': 'initial_state_variance'}
- {'id': 'control_variance', 'dimensions': '1,type=float', 'original_id': 'control_variance'}
- {'id': 'goal_constraint_variance', 'dimensions': '1,type=float', 'original_id': 'goal_constraint_variance'}
- {'id': 'gamma_shape', 'dimensions': '1,type=float', 'original_id': 'gamma_shape'}
- {'id': 'gamma_scale_factor', 'dimensions': '1,type=float', 'original_id': 'gamma_scale_factor'}
- {'id': 'x_limits', 'dimensions': '2,type=float', 'original_id': 'x_limits'}
- {'id': 'y_limits', 'dimensions': '2,type=float', 'original_id': 'y_limits'}
- {'id': 'fps', 'dimensions': '1,type=int', 'original_id': 'fps'}
- {'id': 'heatmap_resolution', 'dimensions': '1,type=int', 'original_id': 'heatmap_resolution'}
- {'id': 'plot_width', 'dimensions': '1,type=int', 'original_id': 'plot_width'}
- {'id': 'plot_height', 'dimensions': '1,type=int', 'original_id': 'plot_height'}
- {'id': 'agent_alpha', 'dimensions': '1,type=float', 'original_id': 'agent_alpha'}
- {'id': 'target_alpha', 'dimensions': '1,type=float', 'original_id': 'target_alpha'}
- {'id': 'color_palette', 'dimensions': '1,type=string', 'original_id': 'color_palette'}
- {'id': 'door_obstacle_center_1', 'dimensions': '2,type=float', 'original_id': 'door_obstacle_center_1'}
- {'id': 'door_obstacle_size_1', 'dimensions': '2,type=float', 'original_id': 'door_obstacle_size_1'}
- {'id': 'door_obstacle_center_2', 'dimensions': '2,type=float', 'original_id': 'door_obstacle_center_2'}
- {'id': 'door_obstacle_size_2', 'dimensions': '2,type=float', 'original_id': 'door_obstacle_size_2'}
- {'id': 'wall_obstacle_center', 'dimensions': '2,type=float', 'original_id': 'wall_obstacle_center'}
- {'id': 'wall_obstacle_size', 'dimensions': '2,type=float', 'original_id': 'wall_obstacle_size'}
- {'id': 'combined_obstacle_center_1', 'dimensions': '2,type=float', 'original_id': 'combined_obstacle_center_1'}
- {'id': 'combined_obstacle_size_1', 'dimensions': '2,type=float', 'original_id': 'combined_obstacle_size_1'}
- {'id': 'combined_obstacle_center_2', 'dimensions': '2,type=float', 'original_id': 'combined_obstacle_center_2'}
- {'id': 'combined_obstacle_size_2', 'dimensions': '2,type=float', 'original_id': 'combined_obstacle_size_2'}
- {'id': 'combined_obstacle_center_3', 'dimensions': '2,type=float', 'original_id': 'combined_obstacle_center_3'}
- {'id': 'combined_obstacle_size_3', 'dimensions': '2,type=float', 'original_id': 'combined_obstacle_size_3'}
- {'id': 'agent1_id', 'dimensions': '1,type=int', 'original_id': 'agent1_id'}
- {'id': 'agent1_radius', 'dimensions': '1,type=float', 'original_id': 'agent1_radius'}
- {'id': 'agent1_initial_position', 'dimensions': '2,type=float', 'original_id': 'agent1_initial_position'}
- {'id': 'agent1_target_position', 'dimensions': '2,type=float', 'original_id': 'agent1_target_position'}
- {'id': 'agent2_id', 'dimensions': '1,type=int', 'original_id': 'agent2_id'}
- {'id': 'agent2_radius', 'dimensions': '1,type=float', 'original_id': 'agent2_radius'}
- {'id': 'agent2_initial_position', 'dimensions': '2,type=float', 'original_id': 'agent2_initial_position'}
- {'id': 'agent2_target_position', 'dimensions': '2,type=float', 'original_id': 'agent2_target_position'}
- {'id': 'agent3_id', 'dimensions': '1,type=int', 'original_id': 'agent3_id'}
- {'id': 'agent3_radius', 'dimensions': '1,type=float', 'original_id': 'agent3_radius'}
- {'id': 'agent3_initial_position', 'dimensions': '2,type=float', 'original_id': 'agent3_initial_position'}
- {'id': 'agent3_target_position', 'dimensions': '2,type=float', 'original_id': 'agent3_target_position'}
- {'id': 'agent4_id', 'dimensions': '1,type=int', 'original_id': 'agent4_id'}
- {'id': 'agent4_radius', 'dimensions': '1,type=float', 'original_id': 'agent4_radius'}
- {'id': 'agent4_initial_position', 'dimensions': '2,type=float', 'original_id': 'agent4_initial_position'}
- {'id': 'agent4_target_position', 'dimensions': '2,type=float', 'original_id': 'agent4_target_position'}
- {'id': 'experiment_seeds', 'dimensions': '2,type=int', 'original_id': 'experiment_seeds'}
- {'id': 'results_dir', 'dimensions': '1,type=string', 'original_id': 'results_dir'}
- {'id': 'animation_template', 'dimensions': '1,type=string', 'original_id': 'animation_template'}
- {'id': 'control_vis_filename', 'dimensions': '1,type=string', 'original_id': 'control_vis_filename'}
- {'id': 'obstacle_distance_filename', 'dimensions': '1,type=string', 'original_id': 'obstacle_distance_filename'}
- {'id': 'path_uncertainty_filename', 'dimensions': '1,type=string', 'original_id': 'path_uncertainty_filename'}
- {'id': 'convergence_filename', 'dimensions': '1,type=string', 'original_id': 'convergence_filename'}
--- CONNECTIONS ---
- {'sources': ['dt'], 'operator': '>', 'targets': ['A'], 'attributes': {}}
- {'sources': ['A', 'B', 'C'], 'operator': '>', 'targets': ['state_space_model'], 'attributes': {}}
- {'sources': ['state_space_model', 'initial_state_variance', 'control_variance'], 'operator': '>', 'targets': ['agent_trajectories'], 'attributes': {}}
- {'sources': ['agent_trajectories', 'goal_constraint_variance'], 'operator': '>', 'targets': ['goal_directed_behavior'], 'attributes': {}}
- {'sources': ['agent_trajectories', 'gamma', 'gamma_shape', 'gamma_scale_factor'], 'operator': '>', 'targets': ['obstacle_avoidance'], 'attributes': {}}
- {'sources': ['agent_trajectories', 'nr_agents'], 'operator': '>', 'targets': ['collision_avoidance'], 'attributes': {}}
- {'sources': ['goal_directed_behavior', 'obstacle_avoidance', 'collision_avoidance'], 'operator': '>', 'targets': ['planning_system'], 'attributes': {}}
--- INITIALPARAMETERIZATION ---
- dt: 1.0
- gamma: 1.0
- nr_steps: 40
- nr_iterations: 350
- nr_agents: 4
- softmin_temperature: 10.0
- intermediate_steps: 10
- save_intermediates: false
# State space matrices
# A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1]
- A: [(1.0, 1.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.0, 0.0, 1.0)]
- B: [(0.0, 0.0), (1.0, 0.0), (0.0, 0.0), (0.0, 1.0)]
- C: [(1.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)]
- initial_state_variance: 100.0
- control_variance: 0.1
- goal_constraint_variance: 1e-05
- gamma_shape: 1.5
- gamma_scale_factor: 0.5
- x_limits: [-20, 20]
- y_limits: [-20, 20]
- fps: 15
- heatmap_resolution: 100
- plot_width: 800
- plot_height: 400
- agent_alpha: 1.0
... (file truncated, total lines: 138)Other Files
GNN Visualizations (Step 6)
Visualizations for pymdp_pomdp_agent: pymdp_pomdp_agent
Images
combined_matrices.png
combined_visualization.png
connections.png
matrix_Am0.png
matrix_Am1.png
matrix_Am2.png
matrix_Bf0.png
matrix_Bf1.png
matrix_Cm0.png
matrix_Cm1.png
matrix_Cm2.png
matrix_Df0.png
matrix_Df1.png
ontology_annotations.png
state_space.png
Markdown Reports
file_content.md
GNN File: src/gnn/examples/pymdp_pomdp_agent.md\n\n## Raw File Content\n\n```\n# GNN Example: Multifactor PyMDP Agent
Format: Markdown representation of a Multifactor PyMDP model in Active Inference format
Version: 1.0
This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.
GNNSection
MultifactorPyMDPAgent
GNNVersionAndFlags
GNN v1
ModelName
Multifactor PyMDP Agent v1
ModelAnnotation
This model represents a PyMDP agent with multiple observation modalities and hidden state factors. - Observation modalities: "state_observation" (3 outcomes), "reward" (3 outcomes), "decision_proprioceptive" (3 outcomes) - Hidden state factors: "reward_level" (2 states), "decision_state" (3 states) - Control: "decision_state" factor is controllable with 3 possible actions. The parameterization is derived from a PyMDP Python script example.
StateSpaceBlock
A_matrices are defined per modality: A_m[observation_outcomes, state_factor0_states, state_factor1_states]
A_m0[3,2,3,type=float] # Likelihood for modality 0 ("state_observation") A_m1[3,2,3,type=float] # Likelihood for modality 1 ("reward") A_m2[3,2,3,type=float] # Likelihood for modality 2 ("decision_proprioceptive")
B_matrices are defined per hidden state factor: B_f[states_next, states_previous, actions]
B_f0[2,2,1,type=float] # Transitions for factor 0 ("reward_level"), 1 implicit action (uncontrolled) B_f1[3,3,3,type=float] # Transitions for factor 1 ("decision_state"), 3 actions
C_vectors are defined per modality: C_m[observation_outcomes]
C_m0[3,type=float] # Preferences for modality 0 C_m1[3,type=float] # Preferences for modality 1 C_m2[3,type=float] # Preferences for modality 2
D_vectors are defined per hidden state factor: D_f[states]
D_f0[2,type=float] # Prior for factor 0 D_f1[3,type=float] # Prior for factor 1
Hidden States
s_f0[2,1,type=float] # Hidden state for factor 0 ("reward_level") s_f1[3,1,type=float] # Hidden state for factor 1 ("decision_state") s_prime_f0[2,1,type=float] # Next hidden state for factor 0 s_prime_f1[3,1,type=float] # Next hidden state for factor 1
Observations
o_m0[3,1,type=float] # Observation for modality 0 o_m1[3,1,type=float] # Observation for modality 1 o_m2[3,1,type=float] # Observation for modality 2
Policy and Control
π_f1[3,type=float] # Policy (distribution over actions) for controllable factor 1 u_f1[1,type=int] # Action taken for controllable factor 1 G[1,type=float] # Expected Free Energy (overall, or can be per policy) t[1,type=int] # Time step
Connections
(D_f0,D_f1)-(s_f0,s_f1) (s_f0,s_f1)-(A_m0,A_m1,A_m2) (A_m0,A_m1,A_m2)-(o_m0,o_m1,o_m2) (s_f0,s_f1,u_f1)-(B_f0,B_f1) # u_f1 primarily affects B_f1; B_f0 is uncontrolled (B_f0,B_f1)-(s_prime_f0,s_prime_f1) (C_m0,C_m1,C_m2)>G G>π_f1 π_f1-u_f1 G=ExpectedFreeEnergy t=Time
InitialParameterization
A_m0: num_obs[0]=3, num_states[0]=2, num_states[1]=3. Format: A[obs_idx][state_f0_idx][state_f1_idx]
A[0][:, :, 0] = np.ones((3,2))/3
A[0][:, :, 1] = np.ones((3,2))/3
A[0][:, :, 2] = [[0.8,0.2],[0.0,0.0],[0.2,0.8]] (obs x state_f0 for state_f1=2)
A_m0={ ( (0.33333,0.33333,0.8), (0.33333,0.33333,0.2) ), # obs=0; (vals for s_f1 over s_f0=0), (vals for s_f1 over s_f0=1) ( (0.33333,0.33333,0.0), (0.33333,0.33333,0.0) ), # obs=1 ( (0.33333,0.33333,0.2), (0.33333,0.33333,0.8) ) # obs=2 }
A_m1: num_obs[1]=3, num_states[0]=2, num_states[1]=3
A[1][2, :, 0] = [1.0,1.0]
A[1][0:2, :, 1] = softmax([[1,0],[0,1]]) approx [[0.731,0.269],[0.269,0.731]]
A[1][2, :, 2] = [1.0,1.0]
Others are 0.
A_m1={ ( (0.0,0.731,0.0), (0.0,0.269,0.0) ), # obs=0 ( (0.0,0.269,0.0), (0.0,0.731,0.0) ), # obs=1 ( (1.0,0.0,1.0), (1.0,0.0,1.0) ) # obs=2 }
A_m2: num_obs[2]=3, num_states[0]=2, num_states[1]=3
A[2][0,:,0]=1.0; A[2][1,:,1]=1.0; A[2][2,:,2]=1.0
Others are 0.
A_m2={ ( (1.0,0.0,0.0), (1.0,0.0,0.0) ), # obs=0 ( (0.0,1.0,0.0), (0.0,1.0,0.0) ), # obs=1 ( (0.0,0.0,1.0), (0.0,0.0,1.0) ) # obs=2 }
B_f0: factor 0 (2 states), uncontrolled (1 action). Format B[s_next, s_prev, action=0]
B_f0 = eye(2)
B_f0={ ( (1.0),(0.0) ), # s_next=0; (vals for s_prev over action=0) ( (0.0),(1.0) ) # s_next=1 }
B_f1: factor 1 (3 states), 3 actions. Format B[s_next, s_prev, action_idx]
B_f1[:,:,action_idx] = eye(3) for each action
B_f1={ ( (1.0,1.0,1.0), (0.0,0.0,0.0), (0.0,0.0,0.0) ), # s_next=0; (vals for actions over s_prev=0), (vals for actions over s_prev=1), ... ( (0.0,0.0,0.0), (1.0,1.0,1.0), (0.0,0.0,0.0) ), # s_next=1 ( (0.0,0.0,0.0), (0.0,0.0,0.0), (1.0,1.0,1.0) ) # s_next=2 }
C_m0: num_obs[0]=3. Defaults to zeros.
C_m0={(0.0,0.0,0.0)}
C_m1: num_obs[1]=3. C[1][0]=1.0, C[1][1]=-2.0
C_m1={(1.0,-2.0,0.0)}
C_m2: num_obs[2]=3. Defaults to zeros.
C_m2={(0.0,0.0,0.0)}
D_f0: factor 0 (2 states). Uniform prior.
D_f0={(0.5,0.5)}
D_f1: factor 1 (3 states). Uniform prior.
D_f1={(0.33333,0.33333,0.33333)}
Equations
Standard PyMDP agent equations for state inference (infer_states),
policy inference (infer_policies), and action sampling (sample_action).
qs = infer_states(o)
q_pi, efe = infer_policies()
action = sample_action()
Time
Dynamic DiscreteTime=t ModelTimeHorizon=Unbounded # Agent definition is generally unbounded, specific simulation runs have a horizon.
ActInfOntologyAnnotation
A_m0=LikelihoodMatrixModality0 A_m1=LikelihoodMatrixModality1 A_m2=LikelihoodMatrixModality2 B_f0=TransitionMatrixFactor0 B_f1=TransitionMatrixFactor1 C_m0=LogPreferenceVectorModality0 C_m1=LogPreferenceVectorModality1 C_m2=LogPreferenceVectorModality2 D_f0=PriorOverHiddenStatesFactor0 D_f1=PriorOverHiddenStatesFactor1 s_f0=HiddenStateFactor0 s_f1=HiddenStateFactor1 s_prime_f0=NextHiddenStateFactor0 s_prime_f1=NextHiddenStateFactor1 o_m0=ObservationModality0 o_m1=ObservationModality1 o_m2=ObservationModality2 π_f1=PolicyVectorFactor1 # Distribution over actions for factor 1 u_f1=ActionFactor1 # Chosen action for factor 1 G=ExpectedFreeEnergy
ModelParameters
num_hidden_states_factors: [2, 3] # s_f0[2], s_f1[3] num_obs_modalities: [3, 3, 3] # o_m0[3], o_m1[3], o_m2[3] num_control_factors: [1, 3] # B_f0 actions_dim=1 (uncontrolled), B_f1 actions_dim=3 (controlled by pi_f1)
Footer
Multifactor PyMDP Agent v1 - GNN Representation
Signature
NA \n```\n\n## Parsed Sections
_HeaderComments
# GNN Example: Multifactor PyMDP Agent
# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format
# Version: 1.0
# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.
GNNSection
MultifactorPyMDPAgent
GNNVersionAndFlags
GNN v1
ModelName
Multifactor PyMDP Agent v1
ModelAnnotation
This model represents a PyMDP agent with multiple observation modalities and hidden state factors.
- Observation modalities: "state_observation" (3 outcomes), "reward" (3 outcomes), "decision_proprioceptive" (3 outcomes)
- Hidden state factors: "reward_level" (2 states), "decision_state" (3 states)
- Control: "decision_state" factor is controllable with 3 possible actions.
The parameterization is derived from a PyMDP Python script example.
StateSpaceBlock
# A_matrices are defined per modality: A_m[observation_outcomes, state_factor0_states, state_factor1_states]
A_m0[3,2,3,type=float] # Likelihood for modality 0 ("state_observation")
A_m1[3,2,3,type=float] # Likelihood for modality 1 ("reward")
A_m2[3,2,3,type=float] # Likelihood for modality 2 ("decision_proprioceptive")
# B_matrices are defined per hidden state factor: B_f[states_next, states_previous, actions]
B_f0[2,2,1,type=float] # Transitions for factor 0 ("reward_level"), 1 implicit action (uncontrolled)
B_f1[3,3,3,type=float] # Transitions for factor 1 ("decision_state"), 3 actions
# C_vectors are defined per modality: C_m[observation_outcomes]
C_m0[3,type=float] # Preferences for modality 0
C_m1[3,type=float] # Preferences for modality 1
C_m2[3,type=float] # Preferences for modality 2
# D_vectors are defined per hidden state factor: D_f[states]
D_f0[2,type=float] # Prior for factor 0
D_f1[3,type=float] # Prior for factor 1
# Hidden States
s_f0[2,1,type=float] # Hidden state for factor 0 ("reward_level")
s_f1[3,1,type=float] # Hidden state for factor 1 ("decision_state")
s_prime_f0[2,1,type=float] # Next hidden state for factor 0
s_prime_f1[3,1,type=float] # Next hidden state for factor 1
# Observations
o_m0[3,1,type=float] # Observation for modality 0
o_m1[3,1,type=float] # Observation for modality 1
o_m2[3,1,type=float] # Observation for modality 2
# Policy and Control
π_f1[3,type=float] # Policy (distribution over actions) for controllable factor 1
u_f1[1,type=int] # Action taken for controllable factor 1
G[1,type=float] # Expected Free Energy (overall, or can be per policy)
t[1,type=int] # Time step
Connections
(D_f0,D_f1)-(s_f0,s_f1)
(s_f0,s_f1)-(A_m0,A_m1,A_m2)
(A_m0,A_m1,A_m2)-(o_m0,o_m1,o_m2)
(s_f0,s_f1,u_f1)-(B_f0,B_f1) # u_f1 primarily affects B_f1; B_f0 is uncontrolled
(B_f0,B_f1)-(s_prime_f0,s_prime_f1)
(C_m0,C_m1,C_m2)>G
G>π_f1
π_f1-u_f1
G=ExpectedFreeEnergy
t=Time
InitialParameterization
# A_m0: num_obs[0]=3, num_states[0]=2, num_states[1]=3. Format: A[obs_idx][state_f0_idx][state_f1_idx]
# A[0][:, :, 0] = np.ones((3,2))/3
# A[0][:, :, 1] = np.ones((3,2))/3
# A[0][:, :, 2] = [[0.8,0.2],[0.0,0.0],[0.2,0.8]] (obs x state_f0 for state_f1=2)
A_m0={
( (0.33333,0.33333,0.8), (0.33333,0.33333,0.2) ), # obs=0; (vals for s_f1 over s_f0=0), (vals for s_f1 over s_f0=1)
( (0.33333,0.33333,0.0), (0.33333,0.33333,0.0) ), # obs=1
( (0.33333,0.33333,0.2), (0.33333,0.33333,0.8) ) # obs=2
}
# A_m1: num_obs[1]=3, num_states[0]=2, num_states[1]=3
# A[1][2, :, 0] = [1.0,1.0]
# A[1][0:2, :, 1] = softmax([[1,0],[0,1]]) approx [[0.731,0.269],[0.269,0.731]]
# A[1][2, :, 2] = [1.0,1.0]
# Others are 0.
A_m1={
( (0.0,0.731,0.0), (0.0,0.269,0.0) ), # obs=0
( (0.0,0.269,0.0), (0.0,0.731,0.0) ), # obs=1
( (1.0,0.0,1.0), (1.0,0.0,1.0) ) # obs=2
}
# A_m2: num_obs[2]=3, num_states[0]=2, num_states[1]=3
# A[2][0,:,0]=1.0; A[2][1,:,1]=1.0; A[2][2,:,2]=1.0
# Others are 0.
A_m2={
( (1.0,0.0,0.0), (1.0,0.0,0.0) ), # obs=0
( (0.0,1.0,0.0), (0.0,1.0,0.0) ), # obs=1
( (0.0,0.0,1.0), (0.0,0.0,1.0) ) # obs=2
}
# B_f0: factor 0 (2 states), uncontrolled (1 action). Format B[s_next, s_prev, action=0]
# B_f0 = eye(2)
B_f0={
( (1.0),(0.0) ), # s_next=0; (vals for s_prev over action=0)
( (0.0),(1.0) ) # s_next=1
}
# B_f1: factor 1 (3 states), 3 actions. Format B[s_next, s_prev, action_idx]
# B_f1[:,:,action_idx] = eye(3) for each action
B_f1={
( (1.0,1.0,1.0), (0.0,0.0,0.0), (0.0,0.0,0.0) ), # s_next=0; (vals for actions over s_prev=0), (vals for actions over s_prev=1), ...
( (0.0,0.0,0.0), (1.0,1.0,1.0), (0.0,0.0,0.0) ), # s_next=1
( (0.0,0.0,0.0), (0.0,0.0,0.0), (1.0,1.0,1.0) ) # s_next=2
}
# C_m0: num_obs[0]=3. Defaults to zeros.
C_m0={(0.0,0.0,0.0)}
# C_m1: num_obs[1]=3. C[1][0]=1.0, C[1][1]=-2.0
C_m1={(1.0,-2.0,0.0)}
# C_m2: num_obs[2]=3. Defaults to zeros.
C_m2={(0.0,0.0,0.0)}
# D_f0: factor 0 (2 states). Uniform prior.
D_f0={(0.5,0.5)}
# D_f1: factor 1 (3 states). Uniform prior.
D_f1={(0.33333,0.33333,0.33333)}
Equations
# Standard PyMDP agent equations for state inference (infer_states),
# policy inference (infer_policies), and action sampling (sample_action).
# qs = infer_states(o)
# q_pi, efe = infer_policies()
# action = sample_action()
Time
Dynamic
DiscreteTime=t
ModelTimeHorizon=Unbounded # Agent definition is generally unbounded, specific simulation runs have a horizon.
ActInfOntologyAnnotation
A_m0=LikelihoodMatrixModality0
A_m1=LikelihoodMatrixModality1
A_m2=LikelihoodMatrixModality2
B_f0=TransitionMatrixFactor0
B_f1=TransitionMatrixFactor1
C_m0=LogPreferenceVectorModality0
C_m1=LogPreferenceVectorModality1
C_m2=LogPreferenceVectorModality2
D_f0=PriorOverHiddenStatesFactor0
D_f1=PriorOverHiddenStatesFactor1
s_f0=HiddenStateFactor0
s_f1=HiddenStateFactor1
s_prime_f0=NextHiddenStateFactor0
s_prime_f1=NextHiddenStateFactor1
o_m0=ObservationModality0
o_m1=ObservationModality1
o_m2=ObservationModality2
π_f1=PolicyVectorFactor1 # Distribution over actions for factor 1
u_f1=ActionFactor1 # Chosen action for factor 1
G=ExpectedFreeEnergy
ModelParameters
num_hidden_states_factors: [2, 3] # s_f0[2], s_f1[3]
num_obs_modalities: [3, 3, 3] # o_m0[3], o_m1[3], o_m2[3]
num_control_factors: [1, 3] # B_f0 actions_dim=1 (uncontrolled), B_f1 actions_dim=3 (controlled by pi_f1)
Footer
Multifactor PyMDP Agent v1 - GNN Representation
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JSON Files
full_model_data.json
{
"_HeaderComments": "# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.",
"GNNSection": "MultifactorPyMDPAgent",
"GNNVersionAndFlags": "GNN v1",
"ModelName": "Multifactor PyMDP Agent v1",
"ModelAnnotation": "This model represents a PyMDP agent with multiple observation modalities and hidden state factors.\n- Observation modalities: \"state_observation\" (3 outcomes), \"reward\" (3 outcomes), \"decision_proprioceptive\" (3 outcomes)\n- Hidden state factors: \"reward_level\" (2 states), \"decision_state\" (3 states)\n- Control: \"decision_state\" factor is controllable with 3 possible actions.\nThe parameterization is derived from a PyMDP Python script example.",
"StateSpaceBlock": "# A_matrices are defined per modality: A_m[observation_outcomes, state_factor0_states, state_factor1_states]\nA_m0[3,2,3,type=float] # Likelihood for modality 0 (\"state_observation\")\nA_m1[3,2,3,type=float] # Likelihood for modality 1 (\"reward\")\nA_m2[3,2,3,type=float] # Likelihood for modality 2 (\"decision_proprioceptive\")\n\n# B_matrices are defined per hidden state factor: B_f[states_next, states_previous, actions]\nB_f0[2,2,1,type=float] # Transitions for factor 0 (\"reward_level\"), 1 implicit action (uncontrolled)\nB_f1[3,3,3,type=float] # Transitions for factor 1 (\"decision_state\"), 3 actions\n\n# C_vectors are defined per modality: C_m[observation_outcomes]\nC_m0[3,type=float] # Preferences for modality 0\nC_m1[3,type=float] # Preferences for modality 1\nC_m2[3,type=float] # Preferences for modality 2\n\n# D_vectors are defined per hidden state factor: D_f[states]\nD_f0[2,type=float] # Prior for factor 0\nD_f1[3,type=float] # Prior for factor 1\n\n# Hidden States\ns_f0[2,1,type=float] # Hidden state for factor 0 (\"reward_level\")\ns_f1[3,1,type=float] # Hidden state for factor 1 (\"decision_state\")\ns_prime_f0[2,1,type=float] # Next hidden state for factor 0\ns_prime_f1[3,1,type=float] # Next hidden state for factor 1\n\n# Observations\no_m0[3,1,type=float] # Observation for modality 0\no_m1[3,1,type=float] # Observation for modality 1\no_m2[3,1,type=float] # Observation for modality 2\n\n# Policy and Control\n\u03c0_f1[3,type=float] # Policy (distribution over actions) for controllable factor 1\nu_f1[1,type=int] # Action taken for controllable factor 1\nG[1,type=float] # Expected Free Energy (overall, or can be per policy)\nt[1,type=int] # Time step",
"Connections": "(D_f0,D_f1)-(s_f0,s_f1)\n(s_f0,s_f1)-(A_m0,A_m1,A_m2)\n(A_m0,A_m1,A_m2)-(o_m0,o_m1,o_m2)\n(s_f0,s_f1,u_f1)-(B_f0,B_f1) # u_f1 primarily affects B_f1; B_f0 is uncontrolled\n(B_f0,B_f1)-(s_prime_f0,s_prime_f1)\n(C_m0,C_m1,C_m2)>G\nG>\u03c0_f1\n\u03c0_f1-u_f1\nG=ExpectedFreeEnergy\nt=Time",
"InitialParameterization": "# A_m0: num_obs[0]=3, num_states[0]=2, num_states[1]=3. Format: A[obs_idx][state_f0_idx][state_f1_idx]\n# A[0][:, :, 0] = np.ones((3,2))/3\n# A[0][:, :, 1] = np.ones((3,2))/3\n# A[0][:, :, 2] = [[0.8,0.2],[0.0,0.0],[0.2,0.8]] (obs x state_f0 for state_f1=2)\nA_m0={\n ( (0.33333,0.33333,0.8), (0.33333,0.33333,0.2) ), # obs=0; (vals for s_f1 over s_f0=0), (vals for s_f1 over s_f0=1)\n ( (0.33333,0.33333,0.0), (0.33333,0.33333,0.0) ), # obs=1\n ( (0.33333,0.33333,0.2), (0.33333,0.33333,0.8) ) # obs=2\n}\n\n# A_m1: num_obs[1]=3, num_states[0]=2, num_states[1]=3\n# A[1][2, :, 0] = [1.0,1.0]\n# A[1][0:2, :, 1] = softmax([[1,0],[0,1]]) approx [[0.731,0.269],[0.269,0.731]]\n# A[1][2, :, 2] = [1.0,1.0]\n# Others are 0.\nA_m1={\n ( (0.0,0.731,0.0), (0.0,0.269,0.0) ), # obs=0\n ( (0.0,0.269,0.0), (0.0,0.731,0.0) ), # obs=1\n ( (1.0,0.0,1.0), (1.0,0.0,1.0) ) # obs=2\n}\n\n# A_m2: num_obs[2]=3, num_states[0]=2, num_states[1]=3\n# A[2][0,:,0]=1.0; A[2][1,:,1]=1.0; A[2][2,:,2]=1.0\n# Others are 0.\nA_m2={\n ( (1.0,0.0,0.0), (1.0,0.0,0.0) ), # obs=0\n ( (0.0,1.0,0.0), (0.0,1.0,0.0) ), # obs=1\n ( (0.0,0.0,1.0), (0.0,0.0,1.0) ) # obs=2\n}\n\n# B_f0: factor 0 (2 states), uncontrolled (1 action). Format B[s_next, s_prev, action=0]\n# B_f0 = eye(2)\nB_f0={\n ( (1.0),(0.0) ), # s_next=0; (vals for s_prev over action=0)\n ( (0.0),(1.0) ) # s_next=1\n}\n\n# B_f1: factor 1 (3 states), 3 actions. Format B[s_next, s_prev, action_idx]\n# B_f1[:,:,action_idx] = eye(3) for each action\nB_f1={\n ( (1.0,1.0,1.0), (0.0,0.0,0.0), (0.0,0.0,0.0) ), # s_next=0; (vals for actions over s_prev=0), (vals for actions over s_prev=1), ...\n ( (0.0,0.0,0.0), (1.0,1.0,1.0), (0.0,0.0,0.0) ), # s_next=1\n ( (0.0,0.0,0.0), (0.0,0.0,0.0), (1.0,1.0,1.0) ) # s_next=2\n}\n\n# C_m0: num_obs[0]=3. Defaults to zeros.\nC_m0={(0.0,0.0,0.0)}\n\n# C_m1: num_obs[1]=3. C[1][0]=1.0, C[1][1]=-2.0\nC_m1={(1.0,-2.0,0.0)}\n\n# C_m2: num_obs[2]=3. Defaults to zeros.\nC_m2={(0.0,0.0,0.0)}\n\n# D_f0: factor 0 (2 states). Uniform prior.\nD_f0={(0.5,0.5)}\n\n# D_f1: factor 1 (3 states). Uniform prior.\nD_f1={(0.33333,0.33333,0.33333)}",
"Equations": "# Standard PyMDP agent equations for state inference (infer_states),\n# policy inference (infer_policies), and action sampling (sample_action).\n# qs = infer_states(o)\n# q_pi, efe = infer_policies()\n# action = sample_action()",
"Time": "Dynamic\nDiscreteTime=t\nModelTimeHorizon=Unbounded # Agent definition is generally unbounded, specific simulation runs have a horizon.",
"ActInfOntologyAnnotation": "A_m0=LikelihoodMatrixModality0\nA_m1=LikelihoodMatrixModality1\nA_m2=LikelihoodMatrixModality2\nB_f0=TransitionMatrixFactor0\nB_f1=TransitionMatrixFactor1\nC_m0=LogPreferenceVectorModality0\nC_m1=LogPreferenceVectorModality1\nC_m2=LogPreferenceVectorModality2\nD_f0=PriorOverHiddenStatesFactor0\nD_f1=PriorOverHiddenStatesFactor1\ns_f0=HiddenStateFactor0\ns_f1=HiddenStateFactor1\ns_prime_f0=NextHiddenStateFactor0\ns_prime_f1=NextHiddenStateFactor1\no_m0=ObservationModality0\no_m1=ObservationModality1\no_m2=ObservationModality2\n\u03c0_f1=PolicyVectorFactor1 # Distribution over actions for factor 1\nu_f1=ActionFactor1 # Chosen action for factor 1\nG=ExpectedFreeEnergy",
"ModelParameters": "num_hidden_states_factors: [2, 3] # s_f0[2], s_f1[3]\nnum_obs_modalities: [3, 3, 3] # o_m0[3], o_m1[3], o_m2[3]\nnum_control_factors: [1, 3] # B_f0 actions_dim=1 (uncontrolled), B_f1 actions_dim=3 (controlled by pi_f1)",
"Footer": "Multifactor PyMDP Agent v1 - GNN Representation",
"Signature": "NA"
}model_metadata.json
{
"ModelName": "Multifactor PyMDP Agent v1",
"ModelAnnotation": "This model represents a PyMDP agent with multiple observation modalities and hidden state factors.\n- Observation modalities: \"state_observation\" (3 outcomes), \"reward\" (3 outcomes), \"decision_proprioceptive\" (3 outcomes)\n- Hidden state factors: \"reward_level\" (2 states), \"decision_state\" (3 states)\n- Control: \"decision_state\" factor is controllable with 3 possible actions.\nThe parameterization is derived from a PyMDP Python script example.",
"GNNVersionAndFlags": "GNN v1",
"Time": "Dynamic\nDiscreteTime=t\nModelTimeHorizon=Unbounded # Agent definition is generally unbounded, specific simulation runs have a horizon.",
"ActInfOntologyAnnotation": "A_m0=LikelihoodMatrixModality0\nA_m1=LikelihoodMatrixModality1\nA_m2=LikelihoodMatrixModality2\nB_f0=TransitionMatrixFactor0\nB_f1=TransitionMatrixFactor1\nC_m0=LogPreferenceVectorModality0\nC_m1=LogPreferenceVectorModality1\nC_m2=LogPreferenceVectorModality2\nD_f0=PriorOverHiddenStatesFactor0\nD_f1=PriorOverHiddenStatesFactor1\ns_f0=HiddenStateFactor0\ns_f1=HiddenStateFactor1\ns_prime_f0=NextHiddenStateFactor0\ns_prime_f1=NextHiddenStateFactor1\no_m0=ObservationModality0\no_m1=ObservationModality1\no_m2=ObservationModality2\n\u03c0_f1=PolicyVectorFactor1 # Distribution over actions for factor 1\nu_f1=ActionFactor1 # Chosen action for factor 1\nG=ExpectedFreeEnergy"
}Visualizations for rxinfer_multiagent_gnn: rxinfer_multiagent_gnn
Images
combined_matrices.png
combined_visualization.png
connections.png
matrix_A.png
matrix_B.png
matrix_C.png
matrix_agent1initialposition.png
matrix_agent1targetposition.png
matrix_agent2initialposition.png
matrix_agent2targetposition.png
matrix_agent3initialposition.png
matrix_agent3targetposition.png
matrix_agent4initialposition.png
matrix_agent4targetposition.png
matrix_combinedobstaclecenter1.png
matrix_combinedobstaclecenter2.png
matrix_combinedobstaclecenter3.png
matrix_combinedobstaclesize1.png
matrix_combinedobstaclesize2.png
matrix_combinedobstaclesize3.png
matrix_doorobstaclecenter1.png
matrix_doorobstaclecenter2.png
matrix_doorobstaclesize1.png
matrix_doorobstaclesize2.png
matrix_experimentseeds.png
matrix_wallobstaclecenter.png
matrix_wallobstaclesize.png
matrix_xlimits.png
matrix_ylimits.png
ontology_annotations.png
state_space.png
Markdown Reports
file_content.md
GNN File: src/gnn/examples/rxinfer_multiagent_gnn.md\n\n## Raw File Content\n\n```\n# GNN Example: RxInfer Multi-agent Trajectory Planning
Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl
Version: 1.0
This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.
GNNSection
RxInferMultiAgentTrajectoryPlanning
GNNVersionAndFlags
GNN v1
ModelName
Multi-agent Trajectory Planning
ModelAnnotation
This model represents a multi-agent trajectory planning scenario in RxInfer.jl. It includes: - State space model for agents moving in a 2D environment - Obstacle avoidance constraints - Goal-directed behavior - Inter-agent collision avoidance The model can be used to simulate trajectory planning in various environments with obstacles.
StateSpaceBlock
Model parameters
dt[1,type=float] # Time step for the state space model gamma[1,type=float] # Constraint parameter for the Halfspace node nr_steps[1,type=int] # Number of time steps in the trajectory nr_iterations[1,type=int] # Number of inference iterations nr_agents[1,type=int] # Number of agents in the simulation softmin_temperature[1,type=float] # Temperature parameter for the softmin function intermediate_steps[1,type=int] # Intermediate results saving interval save_intermediates[1,type=bool] # Whether to save intermediate results
State space matrices
A[4,4,type=float] # State transition matrix B[4,2,type=float] # Control input matrix C[2,4,type=float] # Observation matrix
Prior distributions
initial_state_variance[1,type=float] # Prior on initial state control_variance[1,type=float] # Prior on control inputs goal_constraint_variance[1,type=float] # Goal constraints variance gamma_shape[1,type=float] # Parameters for GammaShapeRate prior gamma_scale_factor[1,type=float] # Parameters for GammaShapeRate prior
Visualization parameters
x_limits[2,type=float] # Plot boundaries (x-axis) y_limits[2,type=float] # Plot boundaries (y-axis) fps[1,type=int] # Animation frames per second heatmap_resolution[1,type=int] # Heatmap resolution plot_width[1,type=int] # Plot width plot_height[1,type=int] # Plot height agent_alpha[1,type=float] # Visualization alpha for agents target_alpha[1,type=float] # Visualization alpha for targets color_palette[1,type=string] # Color palette for visualization
Environment definitions
door_obstacle_center_1[2,type=float] # Door environment, obstacle 1 center door_obstacle_size_1[2,type=float] # Door environment, obstacle 1 size door_obstacle_center_2[2,type=float] # Door environment, obstacle 2 center door_obstacle_size_2[2,type=float] # Door environment, obstacle 2 size
wall_obstacle_center[2,type=float] # Wall environment, obstacle center wall_obstacle_size[2,type=float] # Wall environment, obstacle size
combined_obstacle_center_1[2,type=float] # Combined environment, obstacle 1 center combined_obstacle_size_1[2,type=float] # Combined environment, obstacle 1 size combined_obstacle_center_2[2,type=float] # Combined environment, obstacle 2 center combined_obstacle_size_2[2,type=float] # Combined environment, obstacle 2 size combined_obstacle_center_3[2,type=float] # Combined environment, obstacle 3 center combined_obstacle_size_3[2,type=float] # Combined environment, obstacle 3 size
Agent configurations
agent1_id[1,type=int] # Agent 1 ID agent1_radius[1,type=float] # Agent 1 radius agent1_initial_position[2,type=float] # Agent 1 initial position agent1_target_position[2,type=float] # Agent 1 target position
agent2_id[1,type=int] # Agent 2 ID agent2_radius[1,type=float] # Agent 2 radius agent2_initial_position[2,type=float] # Agent 2 initial position agent2_target_position[2,type=float] # Agent 2 target position
agent3_id[1,type=int] # Agent 3 ID agent3_radius[1,type=float] # Agent 3 radius agent3_initial_position[2,type=float] # Agent 3 initial position agent3_target_position[2,type=float] # Agent 3 target position
agent4_id[1,type=int] # Agent 4 ID agent4_radius[1,type=float] # Agent 4 radius agent4_initial_position[2,type=float] # Agent 4 initial position agent4_target_position[2,type=float] # Agent 4 target position
Experiment configurations
experiment_seeds[2,type=int] # Random seeds for reproducibility results_dir[1,type=string] # Base directory for results animation_template[1,type=string] # Filename template for animations control_vis_filename[1,type=string] # Filename for control visualization obstacle_distance_filename[1,type=string] # Filename for obstacle distance plot path_uncertainty_filename[1,type=string] # Filename for path uncertainty plot convergence_filename[1,type=string] # Filename for convergence plot
Connections
Model parameters
dt > A (A, B, C) > state_space_model
Agent trajectories
(state_space_model, initial_state_variance, control_variance) > agent_trajectories
Goal constraints
(agent_trajectories, goal_constraint_variance) > goal_directed_behavior
Obstacle avoidance
(agent_trajectories, gamma, gamma_shape, gamma_scale_factor) > obstacle_avoidance
Collision avoidance
(agent_trajectories, nr_agents) > collision_avoidance
Complete planning system
(goal_directed_behavior, obstacle_avoidance, collision_avoidance) > planning_system
InitialParameterization
Model parameters
dt=1.0 gamma=1.0 nr_steps=40 nr_iterations=350 nr_agents=4 softmin_temperature=10.0 intermediate_steps=10 save_intermediates=false
State space matrices
A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1]
A={(1.0, 1.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.0, 0.0, 1.0)}
B = [0 0; dt 0; 0 0; 0 dt]
B={(0.0, 0.0), (1.0, 0.0), (0.0, 0.0), (0.0, 1.0)}
C = [1 0 0 0; 0 0 1 0]
C={(1.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)}
Prior distributions
initial_state_variance=100.0 control_variance=0.1 goal_constraint_variance=0.00001 gamma_shape=1.5 gamma_scale_factor=0.5
Visualization parameters
x_limits={(-20, 20)} y_limits={(-20, 20)} fps=15 heatmap_resolution=100 plot_width=800 plot_height=400 agent_alpha=1.0 target_alpha=0.2 color_palette="tab10"
Environment definitions
door_obstacle_center_1={(-40.0, 0.0)} door_obstacle_size_1={(70.0, 5.0)} door_obstacle_center_2={(40.0, 0.0)} door_obstacle_size_2={(70.0, 5.0)}
wall_obstacle_center={(0.0, 0.0)} wall_obstacle_size={(10.0, 5.0)}
combined_obstacle_center_1={(-50.0, 0.0)} combined_obstacle_size_1={(70.0, 2.0)} combined_obstacle_center_2={(50.0, 0.0)} combined_obstacle_size_2={(70.0, 2.0)} combined_obstacle_center_3={(5.0, -1.0)} combined_obstacle_size_3={(3.0, 10.0)}
Agent configurations
agent1_id=1 agent1_radius=2.5 agent1_initial_position={(-4.0, 10.0)} agent1_target_position={(-10.0, -10.0)}
agent2_id=2 agent2_radius=1.5 agent2_initial_position={(-10.0, 5.0)} agent2_target_position={(10.0, -15.0)}
agent3_id=3 agent3_radius=1.0 agent3_initial_position={(-15.0, -10.0)} agent3_target_position={(10.0, 10.0)}
agent4_id=4 agent4_radius=2.5 agent4_initial_position={(0.0, -10.0)} agent4_target_position={(-10.0, 15.0)}
Experiment configurations
experiment_seeds={(42, 123)} results_dir="results" animation_template="{environment}_{seed}.gif" control_vis_filename="control_signals.gif" obstacle_distance_filename="obstacle_distance.png" path_uncertainty_filename="path_uncertainty.png" convergence_filename="convergence.png"
Equations
State space model:
x_{t+1} = A * x_t + B * u_t + w_t, w_t ~ N(0, control_variance)
y_t = C * x_t + v_t, v_t ~ N(0, observation_variance)
Obstacle avoidance constraint:
p(x_t | obstacle) ~ N(d(x_t, obstacle), gamma)
where d(x_t, obstacle) is the distance from position x_t to the nearest obstacle
Goal constraint:
p(x_T | goal) ~ N(goal, goal_constraint_variance)
where x_T is the final position
Collision avoidance constraint:
p(x_i, x_j) ~ N(||x_i - x_j|| - (r_i + r_j), gamma)
where x_i, x_j are positions of agents i and j, r_i, r_j are their radii
Time
Dynamic DiscreteTime ModelTimeHorizon=nr_steps
ActInfOntologyAnnotation
dt=TimeStep gamma=ConstraintParameter nr_steps=TrajectoryLength nr_iterations=InferenceIterations nr_agents=NumberOfAgents softmin_temperature=SoftminTemperature A=StateTransitionMatrix B=ControlInputMatrix C=ObservationMatrix initial_state_variance=InitialStateVariance control_variance=ControlVariance goal_constraint_variance=GoalConstraintVariance
ModelParameters
nr_agents=4 nr_steps=40 nr_iterations=350
Footer
Multi-agent Trajectory Planning - GNN Representation for RxInfer.jl
Signature
Creator: AI Assistant for GNN Date: 2024-07-27 Status: Example for RxInfer.jl multi-agent trajectory planning \n```\n\n## Parsed Sections
_HeaderComments
# GNN Example: RxInfer Multi-agent Trajectory Planning
# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl
# Version: 1.0
# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.
GNNSection
RxInferMultiAgentTrajectoryPlanning
GNNVersionAndFlags
GNN v1
ModelName
Multi-agent Trajectory Planning
ModelAnnotation
This model represents a multi-agent trajectory planning scenario in RxInfer.jl.
It includes:
- State space model for agents moving in a 2D environment
- Obstacle avoidance constraints
- Goal-directed behavior
- Inter-agent collision avoidance
The model can be used to simulate trajectory planning in various environments with obstacles.
StateSpaceBlock
# Model parameters
dt[1,type=float] # Time step for the state space model
gamma[1,type=float] # Constraint parameter for the Halfspace node
nr_steps[1,type=int] # Number of time steps in the trajectory
nr_iterations[1,type=int] # Number of inference iterations
nr_agents[1,type=int] # Number of agents in the simulation
softmin_temperature[1,type=float] # Temperature parameter for the softmin function
intermediate_steps[1,type=int] # Intermediate results saving interval
save_intermediates[1,type=bool] # Whether to save intermediate results
# State space matrices
A[4,4,type=float] # State transition matrix
B[4,2,type=float] # Control input matrix
C[2,4,type=float] # Observation matrix
# Prior distributions
initial_state_variance[1,type=float] # Prior on initial state
control_variance[1,type=float] # Prior on control inputs
goal_constraint_variance[1,type=float] # Goal constraints variance
gamma_shape[1,type=float] # Parameters for GammaShapeRate prior
gamma_scale_factor[1,type=float] # Parameters for GammaShapeRate prior
# Visualization parameters
x_limits[2,type=float] # Plot boundaries (x-axis)
y_limits[2,type=float] # Plot boundaries (y-axis)
fps[1,type=int] # Animation frames per second
heatmap_resolution[1,type=int] # Heatmap resolution
plot_width[1,type=int] # Plot width
plot_height[1,type=int] # Plot height
agent_alpha[1,type=float] # Visualization alpha for agents
target_alpha[1,type=float] # Visualization alpha for targets
color_palette[1,type=string] # Color palette for visualization
# Environment definitions
door_obstacle_center_1[2,type=float] # Door environment, obstacle 1 center
door_obstacle_size_1[2,type=float] # Door environment, obstacle 1 size
door_obstacle_center_2[2,type=float] # Door environment, obstacle 2 center
door_obstacle_size_2[2,type=float] # Door environment, obstacle 2 size
wall_obstacle_center[2,type=float] # Wall environment, obstacle center
wall_obstacle_size[2,type=float] # Wall environment, obstacle size
combined_obstacle_center_1[2,type=float] # Combined environment, obstacle 1 center
combined_obstacle_size_1[2,type=float] # Combined environment, obstacle 1 size
combined_obstacle_center_2[2,type=float] # Combined environment, obstacle 2 center
combined_obstacle_size_2[2,type=float] # Combined environment, obstacle 2 size
combined_obstacle_center_3[2,type=float] # Combined environment, obstacle 3 center
combined_obstacle_size_3[2,type=float] # Combined environment, obstacle 3 size
# Agent configurations
agent1_id[1,type=int] # Agent 1 ID
agent1_radius[1,type=float] # Agent 1 radius
agent1_initial_position[2,type=float] # Agent 1 initial position
agent1_target_position[2,type=float] # Agent 1 target position
agent2_id[1,type=int] # Agent 2 ID
agent2_radius[1,type=float] # Agent 2 radius
agent2_initial_position[2,type=float] # Agent 2 initial position
agent2_target_position[2,type=float] # Agent 2 target position
agent3_id[1,type=int] # Agent 3 ID
agent3_radius[1,type=float] # Agent 3 radius
agent3_initial_position[2,type=float] # Agent 3 initial position
agent3_target_position[2,type=float] # Agent 3 target position
agent4_id[1,type=int] # Agent 4 ID
agent4_radius[1,type=float] # Agent 4 radius
agent4_initial_position[2,type=float] # Agent 4 initial position
agent4_target_position[2,type=float] # Agent 4 target position
# Experiment configurations
experiment_seeds[2,type=int] # Random seeds for reproducibility
results_dir[1,type=string] # Base directory for results
animation_template[1,type=string] # Filename template for animations
control_vis_filename[1,type=string] # Filename for control visualization
obstacle_distance_filename[1,type=string] # Filename for obstacle distance plot
path_uncertainty_filename[1,type=string] # Filename for path uncertainty plot
convergence_filename[1,type=string] # Filename for convergence plot
Connections
# Model parameters
dt > A
(A, B, C) > state_space_model
# Agent trajectories
(state_space_model, initial_state_variance, control_variance) > agent_trajectories
# Goal constraints
(agent_trajectories, goal_constraint_variance) > goal_directed_behavior
# Obstacle avoidance
(agent_trajectories, gamma, gamma_shape, gamma_scale_factor) > obstacle_avoidance
# Collision avoidance
(agent_trajectories, nr_agents) > collision_avoidance
# Complete planning system
(goal_directed_behavior, obstacle_avoidance, collision_avoidance) > planning_system
InitialParameterization
# Model parameters
dt=1.0
gamma=1.0
nr_steps=40
nr_iterations=350
nr_agents=4
softmin_temperature=10.0
intermediate_steps=10
save_intermediates=false
# State space matrices
# A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1]
A={(1.0, 1.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.0, 0.0, 1.0)}
# B = [0 0; dt 0; 0 0; 0 dt]
B={(0.0, 0.0), (1.0, 0.0), (0.0, 0.0), (0.0, 1.0)}
# C = [1 0 0 0; 0 0 1 0]
C={(1.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)}
# Prior distributions
initial_state_variance=100.0
control_variance=0.1
goal_constraint_variance=0.00001
gamma_shape=1.5
gamma_scale_factor=0.5
# Visualization parameters
x_limits={(-20, 20)}
y_limits={(-20, 20)}
fps=15
heatmap_resolution=100
plot_width=800
plot_height=400
agent_alpha=1.0
target_alpha=0.2
color_palette="tab10"
# Environment definitions
door_obstacle_center_1={(-40.0, 0.0)}
door_obstacle_size_1={(70.0, 5.0)}
door_obstacle_center_2={(40.0, 0.0)}
door_obstacle_size_2={(70.0, 5.0)}
wall_obstacle_center={(0.0, 0.0)}
wall_obstacle_size={(10.0, 5.0)}
combined_obstacle_center_1={(-50.0, 0.0)}
combined_obstacle_size_1={(70.0, 2.0)}
combined_obstacle_center_2={(50.0, 0.0)}
combined_obstacle_size_2={(70.0, 2.0)}
combined_obstacle_center_3={(5.0, -1.0)}
combined_obstacle_size_3={(3.0, 10.0)}
# Agent configurations
agent1_id=1
agent1_radius=2.5
agent1_initial_position={(-4.0, 10.0)}
agent1_target_position={(-10.0, -10.0)}
agent2_id=2
agent2_radius=1.5
agent2_initial_position={(-10.0, 5.0)}
agent2_target_position={(10.0, -15.0)}
agent3_id=3
agent3_radius=1.0
agent3_initial_position={(-15.0, -10.0)}
agent3_target_position={(10.0, 10.0)}
agent4_id=4
agent4_radius=2.5
agent4_initial_position={(0.0, -10.0)}
agent4_target_position={(-10.0, 15.0)}
# Experiment configurations
experiment_seeds={(42, 123)}
results_dir="results"
animation_template="{environment}_{seed}.gif"
control_vis_filename="control_signals.gif"
obstacle_distance_filename="obstacle_distance.png"
path_uncertainty_filename="path_uncertainty.png"
convergence_filename="convergence.png"
Equations
# State space model:
# x_{t+1} = A * x_t + B * u_t + w_t, w_t ~ N(0, control_variance)
# y_t = C * x_t + v_t, v_t ~ N(0, observation_variance)
#
# Obstacle avoidance constraint:
# p(x_t | obstacle) ~ N(d(x_t, obstacle), gamma)
# where d(x_t, obstacle) is the distance from position x_t to the nearest obstacle
#
# Goal constraint:
# p(x_T | goal) ~ N(goal, goal_constraint_variance)
# where x_T is the final position
#
# Collision avoidance constraint:
# p(x_i, x_j) ~ N(||x_i - x_j|| - (r_i + r_j), gamma)
# where x_i, x_j are positions of agents i and j, r_i, r_j are their radii
Time
Dynamic
DiscreteTime
ModelTimeHorizon=nr_steps
ActInfOntologyAnnotation
dt=TimeStep
gamma=ConstraintParameter
nr_steps=TrajectoryLength
nr_iterations=InferenceIterations
nr_agents=NumberOfAgents
softmin_temperature=SoftminTemperature
A=StateTransitionMatrix
B=ControlInputMatrix
C=ObservationMatrix
initial_state_variance=InitialStateVariance
control_variance=ControlVariance
goal_constraint_variance=GoalConstraintVariance
ModelParameters
nr_agents=4
nr_steps=40
nr_iterations=350
Footer
Multi-agent Trajectory Planning - GNN Representation for RxInfer.jl
Signature
Creator: AI Assistant for GNN
Date: 2024-07-27
Status: Example for RxInfer.jl multi-agent trajectory planning
JSON Files
full_model_data.json
{
"_HeaderComments": "# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.",
"GNNSection": "RxInferMultiAgentTrajectoryPlanning",
"GNNVersionAndFlags": "GNN v1",
"ModelName": "Multi-agent Trajectory Planning",
"ModelAnnotation": "This model represents a multi-agent trajectory planning scenario in RxInfer.jl.\nIt includes:\n- State space model for agents moving in a 2D environment\n- Obstacle avoidance constraints\n- Goal-directed behavior\n- Inter-agent collision avoidance\nThe model can be used to simulate trajectory planning in various environments with obstacles.",
"StateSpaceBlock": "# Model parameters\ndt[1,type=float] # Time step for the state space model\ngamma[1,type=float] # Constraint parameter for the Halfspace node\nnr_steps[1,type=int] # Number of time steps in the trajectory\nnr_iterations[1,type=int] # Number of inference iterations\nnr_agents[1,type=int] # Number of agents in the simulation\nsoftmin_temperature[1,type=float] # Temperature parameter for the softmin function\nintermediate_steps[1,type=int] # Intermediate results saving interval\nsave_intermediates[1,type=bool] # Whether to save intermediate results\n\n# State space matrices\nA[4,4,type=float] # State transition matrix\nB[4,2,type=float] # Control input matrix\nC[2,4,type=float] # Observation matrix\n\n# Prior distributions\ninitial_state_variance[1,type=float] # Prior on initial state\ncontrol_variance[1,type=float] # Prior on control inputs\ngoal_constraint_variance[1,type=float] # Goal constraints variance\ngamma_shape[1,type=float] # Parameters for GammaShapeRate prior\ngamma_scale_factor[1,type=float] # Parameters for GammaShapeRate prior\n\n# Visualization parameters\nx_limits[2,type=float] # Plot boundaries (x-axis)\ny_limits[2,type=float] # Plot boundaries (y-axis)\nfps[1,type=int] # Animation frames per second\nheatmap_resolution[1,type=int] # Heatmap resolution\nplot_width[1,type=int] # Plot width\nplot_height[1,type=int] # Plot height\nagent_alpha[1,type=float] # Visualization alpha for agents\ntarget_alpha[1,type=float] # Visualization alpha for targets\ncolor_palette[1,type=string] # Color palette for visualization\n\n# Environment definitions\ndoor_obstacle_center_1[2,type=float] # Door environment, obstacle 1 center\ndoor_obstacle_size_1[2,type=float] # Door environment, obstacle 1 size\ndoor_obstacle_center_2[2,type=float] # Door environment, obstacle 2 center\ndoor_obstacle_size_2[2,type=float] # Door environment, obstacle 2 size\n\nwall_obstacle_center[2,type=float] # Wall environment, obstacle center\nwall_obstacle_size[2,type=float] # Wall environment, obstacle size\n\ncombined_obstacle_center_1[2,type=float] # Combined environment, obstacle 1 center\ncombined_obstacle_size_1[2,type=float] # Combined environment, obstacle 1 size\ncombined_obstacle_center_2[2,type=float] # Combined environment, obstacle 2 center\ncombined_obstacle_size_2[2,type=float] # Combined environment, obstacle 2 size\ncombined_obstacle_center_3[2,type=float] # Combined environment, obstacle 3 center\ncombined_obstacle_size_3[2,type=float] # Combined environment, obstacle 3 size\n\n# Agent configurations\nagent1_id[1,type=int] # Agent 1 ID\nagent1_radius[1,type=float] # Agent 1 radius\nagent1_initial_position[2,type=float] # Agent 1 initial position\nagent1_target_position[2,type=float] # Agent 1 target position\n\nagent2_id[1,type=int] # Agent 2 ID\nagent2_radius[1,type=float] # Agent 2 radius\nagent2_initial_position[2,type=float] # Agent 2 initial position\nagent2_target_position[2,type=float] # Agent 2 target position\n\nagent3_id[1,type=int] # Agent 3 ID\nagent3_radius[1,type=float] # Agent 3 radius\nagent3_initial_position[2,type=float] # Agent 3 initial position\nagent3_target_position[2,type=float] # Agent 3 target position\n\nagent4_id[1,type=int] # Agent 4 ID\nagent4_radius[1,type=float] # Agent 4 radius\nagent4_initial_position[2,type=float] # Agent 4 initial position\nagent4_target_position[2,type=float] # Agent 4 target position\n\n# Experiment configurations\nexperiment_seeds[2,type=int] # Random seeds for reproducibility\nresults_dir[1,type=string] # Base directory for results\nanimation_template[1,type=string] # Filename template for animations\ncontrol_vis_filename[1,type=string] # Filename for control visualization\nobstacle_distance_filename[1,type=string] # Filename for obstacle distance plot\npath_uncertainty_filename[1,type=string] # Filename for path uncertainty plot\nconvergence_filename[1,type=string] # Filename for convergence plot",
"Connections": "# Model parameters\ndt > A\n(A, B, C) > state_space_model\n\n# Agent trajectories\n(state_space_model, initial_state_variance, control_variance) > agent_trajectories\n\n# Goal constraints\n(agent_trajectories, goal_constraint_variance) > goal_directed_behavior\n\n# Obstacle avoidance\n(agent_trajectories, gamma, gamma_shape, gamma_scale_factor) > obstacle_avoidance\n\n# Collision avoidance\n(agent_trajectories, nr_agents) > collision_avoidance\n\n# Complete planning system\n(goal_directed_behavior, obstacle_avoidance, collision_avoidance) > planning_system",
"InitialParameterization": "# Model parameters\ndt=1.0\ngamma=1.0\nnr_steps=40\nnr_iterations=350\nnr_agents=4\nsoftmin_temperature=10.0\nintermediate_steps=10\nsave_intermediates=false\n\n# State space matrices\n# A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1]\nA={(1.0, 1.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.0, 0.0, 1.0)}\n\n# B = [0 0; dt 0; 0 0; 0 dt]\nB={(0.0, 0.0), (1.0, 0.0), (0.0, 0.0), (0.0, 1.0)}\n\n# C = [1 0 0 0; 0 0 1 0]\nC={(1.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)}\n\n# Prior distributions\ninitial_state_variance=100.0\ncontrol_variance=0.1\ngoal_constraint_variance=0.00001\ngamma_shape=1.5\ngamma_scale_factor=0.5\n\n# Visualization parameters\nx_limits={(-20, 20)}\ny_limits={(-20, 20)}\nfps=15\nheatmap_resolution=100\nplot_width=800\nplot_height=400\nagent_alpha=1.0\ntarget_alpha=0.2\ncolor_palette=\"tab10\"\n\n# Environment definitions\ndoor_obstacle_center_1={(-40.0, 0.0)}\ndoor_obstacle_size_1={(70.0, 5.0)}\ndoor_obstacle_center_2={(40.0, 0.0)}\ndoor_obstacle_size_2={(70.0, 5.0)}\n\nwall_obstacle_center={(0.0, 0.0)}\nwall_obstacle_size={(10.0, 5.0)}\n\ncombined_obstacle_center_1={(-50.0, 0.0)}\ncombined_obstacle_size_1={(70.0, 2.0)}\ncombined_obstacle_center_2={(50.0, 0.0)}\ncombined_obstacle_size_2={(70.0, 2.0)}\ncombined_obstacle_center_3={(5.0, -1.0)}\ncombined_obstacle_size_3={(3.0, 10.0)}\n\n# Agent configurations\nagent1_id=1\nagent1_radius=2.5\nagent1_initial_position={(-4.0, 10.0)}\nagent1_target_position={(-10.0, -10.0)}\n\nagent2_id=2\nagent2_radius=1.5\nagent2_initial_position={(-10.0, 5.0)}\nagent2_target_position={(10.0, -15.0)}\n\nagent3_id=3\nagent3_radius=1.0\nagent3_initial_position={(-15.0, -10.0)}\nagent3_target_position={(10.0, 10.0)}\n\nagent4_id=4\nagent4_radius=2.5\nagent4_initial_position={(0.0, -10.0)}\nagent4_target_position={(-10.0, 15.0)}\n\n# Experiment configurations\nexperiment_seeds={(42, 123)}\nresults_dir=\"results\"\nanimation_template=\"{environment}_{seed}.gif\"\ncontrol_vis_filename=\"control_signals.gif\"\nobstacle_distance_filename=\"obstacle_distance.png\"\npath_uncertainty_filename=\"path_uncertainty.png\"\nconvergence_filename=\"convergence.png\"",
"Equations": "# State space model:\n# x_{t+1} = A * x_t + B * u_t + w_t, w_t ~ N(0, control_variance)\n# y_t = C * x_t + v_t, v_t ~ N(0, observation_variance)\n#\n# Obstacle avoidance constraint:\n# p(x_t | obstacle) ~ N(d(x_t, obstacle), gamma)\n# where d(x_t, obstacle) is the distance from position x_t to the nearest obstacle\n#\n# Goal constraint:\n# p(x_T | goal) ~ N(goal, goal_constraint_variance)\n# where x_T is the final position\n#\n# Collision avoidance constraint:\n# p(x_i, x_j) ~ N(||x_i - x_j|| - (r_i + r_j), gamma)\n# where x_i, x_j are positions of agents i and j, r_i, r_j are their radii",
"Time": "Dynamic\nDiscreteTime\nModelTimeHorizon=nr_steps",
"ActInfOntologyAnnotation": "dt=TimeStep\ngamma=ConstraintParameter\nnr_steps=TrajectoryLength\nnr_iterations=InferenceIterations\nnr_agents=NumberOfAgents\nsoftmin_temperature=SoftminTemperature\nA=StateTransitionMatrix\nB=ControlInputMatrix\nC=ObservationMatrix\ninitial_state_variance=InitialStateVariance\ncontrol_variance=ControlVariance\ngoal_constraint_variance=GoalConstraintVariance",
"ModelParameters": "nr_agents=4\nnr_steps=40\nnr_iterations=350",
"Footer": "Multi-agent Trajectory Planning - GNN Representation for RxInfer.jl",
"Signature": "Creator: AI Assistant for GNN\nDate: 2024-07-27\nStatus: Example for RxInfer.jl multi-agent trajectory planning"
}model_metadata.json
{
"ModelName": "Multi-agent Trajectory Planning",
"ModelAnnotation": "This model represents a multi-agent trajectory planning scenario in RxInfer.jl.\nIt includes:\n- State space model for agents moving in a 2D environment\n- Obstacle avoidance constraints\n- Goal-directed behavior\n- Inter-agent collision avoidance\nThe model can be used to simulate trajectory planning in various environments with obstacles.",
"GNNVersionAndFlags": "GNN v1",
"Time": "Dynamic\nDiscreteTime\nModelTimeHorizon=nr_steps",
"ActInfOntologyAnnotation": "dt=TimeStep\ngamma=ConstraintParameter\nnr_steps=TrajectoryLength\nnr_iterations=InferenceIterations\nnr_agents=NumberOfAgents\nsoftmin_temperature=SoftminTemperature\nA=StateTransitionMatrix\nB=ControlInputMatrix\nC=ObservationMatrix\ninitial_state_variance=InitialStateVariance\ncontrol_variance=ControlVariance\ngoal_constraint_variance=GoalConstraintVariance"
}MCP Integration Report (Step 7)
🤖 MCP Integration and API Report
🗓️ Report Generated: 2025-06-07 12:29:08
MCP Core Directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/mcp
Project Source Root (for modules): /home/trim/Documents/GitHub/GeneralizedNotationNotation/src
Output Directory for this report: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/mcp_processing_step
🌐 Global Summary of Registered MCP Tools
This section lists all tools currently registered with the MCP system, along with their defining module, arguments, and description.
- Tool:
ensure_directory_exists - Defined in Module:
src.setup.mcp - Arguments (from signature):
(directory_path) - Description: "Ensures a directory exists, creating it if necessary. Returns the absolute path."
- Schema:
json { "directory_path": { "type": "string", "description": "Path of the directory to create if it doesn't exist." } } - Tool:
estimate_resources_for_gnn_directory - Defined in Module:
src.gnn_type_checker.mcp - Arguments (from signature):
(dir_path, recursive) - Description: "Estimates computational resources for all GNN files in a specified directory."
- Schema:
json { "dir_path": { "type": "string", "description": "Path to the directory for GNN resource estimation." }, "recursive": { "type": "boolean", "description": "Search directory recursively. Defaults to False.", "optional": true } } - Tool:
estimate_resources_for_gnn_file - Defined in Module:
src.gnn_type_checker.mcp - Arguments (from signature):
(file_path) - Description: "Estimates computational resources (memory, inference, storage) for a GNN model file."
- Schema:
json { "file_path": { "type": "string", "description": "Path to the GNN file for resource estimation." } } - Tool:
find_project_gnn_files - Defined in Module:
src.setup.mcp - Arguments (from signature):
(search_directory, recursive) - Description: "Finds all GNN (.md) files in a specified directory within the project."
- Schema:
json { "search_directory": { "type": "string", "description": "The directory to search for GNN (.md) files." }, "recursive": { "type": "boolean", "description": "Set to true to search recursively. Defaults to false.", "optional": true } } - Tool:
generate_pipeline_summary_site - Defined in Module:
src.site.mcp - Arguments (from signature):
(output_dir, site_output_filename, verbose) - Description: "Generates a single HTML website summarizing all contents of the GNN pipeline output directory."
- Schema:
json { "type": "object", "properties": { "output_dir": { "type": "string", "description": "The main pipeline output directory to scan for results." }, "site_output_filename": { "type": "string", "description": "The filename for the output HTML report (e.g., 'summary.html')." }, "verbose": { "type": "boolean", "description": "Enable verbose logging for the generator." } }, "required": [ "output_dir", "site_output_filename" ] } - Tool:
get_gnn_documentation - Defined in Module:
src.gnn.mcp - Arguments (from signature):
(doc_name) - Description: "Retrieve the content of a GNN core documentation file (e.g., syntax, file structure)."
- Schema:
json { "doc_name": { "type": "string", "description": "Name of the GNN document (e.g., 'file_structure', 'punctuation')", "enum": [ "file_structure", "punctuation" ] } } - Tool:
get_standard_output_paths - Defined in Module:
src.setup.mcp - Arguments (from signature):
(base_output_directory) - Description: "Gets a dictionary of standard output directory paths (e.g., for type_check, visualization), creating them if needed."
- Schema:
json { "base_output_directory": { "type": "string", "description": "The base directory where output subdirectories will be managed." } } - Tool:
list_render_targets - Defined in Module:
src.render.mcp - Arguments (from signature):
() - Description: "Lists the available target formats for GNN rendering (e.g., pymdp, rxinfer_toml)."
- Schema:
json { "properties": {}, "title": "ListRenderTargetsInput", "type": "object" } - Tool:
llm.explain_gnn_file - Defined in Module:
src.llm.mcp - Arguments (from signature):
(file_path_str, aspect_to_explain) - Description: "Reads a GNN specification file and uses an LLM to generate an explanation of its content. Can focus on a specific aspect if provided."
- Schema:
json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file." }, "aspect_to_explain": { "type": "string", "description": "(Optional) A specific part or concept within the GNN to focus the explanation on." } }, "required": [ "file_path_str" ] } - Tool:
llm.generate_professional_summary - Defined in Module:
src.llm.mcp - Arguments (from signature):
(file_path_str, experiment_details, target_audience) - Description: "Reads a GNN file and optional experiment details, then uses an LLM to generate a professional summary suitable for reports or papers."
- Schema:
json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file." }, "experiment_details": { "type": "string", "description": "(Optional) Text describing the experiments conducted with the model, including setup, results, or observations." }, "target_audience": { "type": "string", "description": "(Optional) The intended audience for the summary (e.g., 'fellow researchers', 'project managers'). Default: 'fellow researchers'." } }, "required": [ "file_path_str" ] } - Tool:
llm.summarize_gnn_file - Defined in Module:
src.llm.mcp - Arguments (from signature):
(file_path_str, user_prompt_suffix) - Description: "Reads a GNN specification file and uses an LLM to generate a concise summary of its content. Optionally, a user prompt suffix can refine the summary focus."
- Schema:
json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file (.md, .gnn.md, .json)." }, "user_prompt_suffix": { "type": "string", "description": "(Optional) Additional instructions or focus points for the summary." } }, "required": [ "file_path_str" ] } - Tool:
parse_gnn_file - Defined in Module:
src.visualization.mcp - Arguments (from signature):
(file_path) - Description: "Parse a GNN file without visualization"
- Schema:
json { "file_path": { "type": "string", "description": "Path to the GNN file to parse" } } - Tool:
render_gnn_specification - Defined in Module:
src.render.mcp - Arguments (from signature):
(input_data) - Description: "Renders a GNN (Generalized Notation Notation) specification into an executable format for a target modeling environment like PyMDP or RxInfer.jl."
- Schema:
json { "properties": { "gnn_specification": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "string" } ], "description": "The GNN specification itself as a dictionary, or a string URI/path to a GNN spec file (e.g., JSON).", "title": "Gnn Specification" }, "target_format": { "description": "The target format to render the GNN specification to.", "enum": [ "pymdp", "rxinfer_toml" ], "title": "Target Format", "type": "string" }, "output_filename_base": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Optional desired base name for the output file (e.g., 'my_model'). Extension is added automatically. If None, derived from GNN spec name or input file name.", "title": "Output Filename Base" }, "render_options": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "default": null, "description": "Optional dictionary of specific options for the chosen renderer (e.g., data_bindings for RxInfer).", "title": "Render Options" } }, "required": [ "gnn_specification", "target_format" ], "title": "RenderGnnInput", "type": "object" } - Tool:
run_gnn_type_checker - Defined in Module:
src.tests.mcp - Arguments (from signature):
(file_path) - Description: "Run the GNN type checker on a specific file (via test module)."
- Schema:
json { "file_path": { "type": "string", "description": "Path to the GNN file to check" } } - Tool:
run_gnn_type_checker_on_directory - Defined in Module:
src.tests.mcp - Arguments (from signature):
(dir_path, report_file) - Description: "Run the GNN type checker on all GNN files in a directory (via test module)."
- Schema:
json { "dir_path": { "type": "string", "description": "Path to directory containing GNN files" }, "report_file": { "type": "string", "description": "Optional path to save the report" } } - Tool:
run_gnn_unit_tests - Defined in Module:
src.tests.mcp - Arguments (from signature):
() - Description: "Run the GNN unit tests and return results."
- Schema:
json No schema provided. - Tool:
sympy_analyze_stability - Defined in Module:
src.mcp.sympy_mcp - Arguments (from signature):
(transition_matrices) - Description: "Analyze system stability using eigenvalue analysis"
- Schema:
json { "type": "object", "properties": { "transition_matrices": { "type": "array", "description": "List of transition matrices to analyze" } }, "required": [ "transition_matrices" ] } - Tool:
sympy_cleanup - Defined in Module:
src.mcp.sympy_mcp - Arguments (from signature):
() - Description: "Clean up SymPy MCP integration and reset state"
- Schema:
json { "type": "object", "properties": {} } - Tool:
sympy_get_latex - Defined in Module:
src.mcp.sympy_mcp - Arguments (from signature):
(expression) - Description: "Convert a mathematical expression to LaTeX format"
- Schema:
json { "type": "object", "properties": { "expression": { "type": "string", "description": "Expression to convert to LaTeX" } }, "required": [ "expression" ] } - Tool:
sympy_initialize - Defined in Module:
src.mcp.sympy_mcp - Arguments (from signature):
(server_executable) - Description: "Initialize SymPy MCP integration"
- Schema:
json { "type": "object", "properties": { "server_executable": { "type": "string", "description": "Path to SymPy MCP server executable", "default": null } } } - Tool:
sympy_simplify_expression - Defined in Module:
src.mcp.sympy_mcp - Arguments (from signature):
(expression) - Description: "Simplify a mathematical expression to canonical form"
- Schema:
json { "type": "object", "properties": { "expression": { "type": "string", "description": "Mathematical expression to simplify" } }, "required": [ "expression" ] } - Tool:
sympy_solve_equation - Defined in Module:
src.mcp.sympy_mcp - Arguments (from signature):
(equation, variable, domain) - Description: "Solve an equation algebraically for a specified variable"
- Schema:
json { "type": "object", "properties": { "equation": { "type": "string", "description": "Equation to solve" }, "variable": { "type": "string", "description": "Variable to solve for" }, "domain": { "type": "string", "description": "Solution domain (COMPLEX, REAL, etc.)", "default": "COMPLEX" } }, "required": [ "equation", "variable" ] } - Tool:
sympy_validate_equation - Defined in Module:
src.mcp.sympy_mcp - Arguments (from signature):
(equation, context) - Description: "Validate a mathematical equation using SymPy symbolic processing"
- Schema:
json { "type": "object", "properties": { "equation": { "type": "string", "description": "Mathematical equation to validate" }, "context": { "type": "object", "description": "GNN context for variable definitions", "default": {} } }, "required": [ "equation" ] } - Tool:
sympy_validate_matrix - Defined in Module:
src.mcp.sympy_mcp - Arguments (from signature):
(matrix_data, matrix_type) - Description: "Validate matrix properties including stochasticity constraints"
- Schema:
json { "type": "object", "properties": { "matrix_data": { "type": "array", "description": "Matrix data as array of arrays" }, "matrix_type": { "type": "string", "description": "Type of matrix (transition, observation, etc.)", "default": "transition" } }, "required": [ "matrix_data" ] } - Tool:
type_check_gnn_directory - Defined in Module:
src.gnn_type_checker.mcp - Arguments (from signature):
(dir_path, recursive, output_dir_base, report_md_filename) - Description: "Runs the GNN type checker on all GNN files in a specified directory. If output_dir_base is provided, reports are generated."
- Schema:
json { "dir_path": { "type": "string", "description": "Path to the directory containing GNN files to be type-checked." }, "recursive": { "type": "boolean", "description": "Search directory recursively. Defaults to False.", "optional": true }, "output_dir_base": { "type": "string", "description": "Optional base directory to save the report and other artifacts (HTML, JSON).", "optional": true }, "report_md_filename": { "type": "string", "description": "Optional filename for the markdown report (e.g., 'my_report.md'). Defaults to 'type_check_report.md'.", "optional": true } } - Tool:
type_check_gnn_file - Defined in Module:
src.gnn_type_checker.mcp - Arguments (from signature):
(file_path) - Description: "Runs the GNN type checker on a specified GNN model file."
- Schema:
json { "file_path": { "type": "string", "description": "Path to the GNN file to be type-checked." } } - Tool:
visualize_gnn_directory - Defined in Module:
src.visualization.mcp - Arguments (from signature):
(dir_path, output_dir) - Description: "Visualize all GNN files in a directory"
- Schema:
json { "dir_path": { "type": "string", "description": "Path to directory containing GNN files" }, "output_dir": { "type": "string", "description": "Optional output directory" } } - Tool:
visualize_gnn_file - Defined in Module:
src.visualization.mcp - Arguments (from signature):
(file_path, output_dir) - Description: "Generate visualizations for a specific GNN file."
- Schema:
json { "file_path": { "type": "string", "description": "Path to the GNN file to visualize" }, "output_dir": { "type": "string", "description": "Optional output directory" } }
🔬 Core MCP File Check
This section verifies the presence of essential MCP files in the core directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/mcp
- ✅
mcp.py: Found (20304 bytes) - ✅
meta_mcp.py: Found (4954 bytes) - ✅
cli.py: Found (4644 bytes) - ✅
server_stdio.py: Found (7620 bytes) - ✅
server_http.py: Found (7731 bytes)
Status: 5/5 core MCP files found. All core files seem present.
🧩 Functional Module MCP Integration & API Check
Checking for mcp.py in these subdirectories of /home/trim/Documents/GitHub/GeneralizedNotationNotation/src: ['export', 'gnn', 'gnn_type_checker', 'ontology', 'setup', 'tests', 'visualization', 'llm']
Module: export (at /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/export)
- ✅
mcp.pyStatus: Found (7976 bytes) - Exposed Methods & Tools:
def _handle_export(export_func, gnn_file_path, output_file_path, format_name, requires_nx)(AST parsed) - *"Generic helper to run an export function and handle common exceptions."def export_gnn_to_gexf_mcp(gnn_file_path, output_file_path)(AST parsed)def export_gnn_to_graphml_mcp(gnn_file_path, output_file_path)(AST parsed)def export_gnn_to_json_adjacency_list_mcp(gnn_file_path, output_file_path)(AST parsed)def export_gnn_to_json_mcp(gnn_file_path, output_file_path)(AST parsed)def export_gnn_to_plaintext_dsl_mcp(gnn_file_path, output_file_path)(AST parsed)def export_gnn_to_plaintext_summary_mcp(gnn_file_path, output_file_path)(AST parsed)def export_gnn_to_python_pickle_mcp(gnn_file_path, output_file_path)(AST parsed)def export_gnn_to_xml_mcp(gnn_file_path, output_file_path)(AST parsed)def register_tools(mcp_instance)(AST parsed) - *"Registers all GNN export tools with the MCP instance."
Module: gnn (at /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn)
- ✅
mcp.pyStatus: Found (4122 bytes) - Exposed Methods & Tools:
def _retrieve_gnn_doc_resource(uri)(AST parsed) - *"Retrieve GNN documentation resource by URI."def get_gnn_documentation(doc_name)- *Description: "Retrieve the content of a GNN core documentation file (e.g., syntax, file structure)."- Schema:
json { "doc_name": { "type": "string", "description": "Name of the GNN document (e.g., 'file_structure', 'punctuation')", "enum": [ "file_structure", "punctuation" ] } }
- Schema:
def register_tools(mcp_instance)(AST parsed) - *"Register GNN documentation tools and resources with the MCP."
Module: gnn_type_checker (at /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn_type_checker)
- ✅
mcp.pyStatus: Found (10921 bytes) - Exposed Methods & Tools:
def estimate_resources_for_gnn_directory(dir_path, recursive)- *Description: "Estimates computational resources for all GNN files in a specified directory."- Schema:
json { "dir_path": { "type": "string", "description": "Path to the directory for GNN resource estimation." }, "recursive": { "type": "boolean", "description": "Search directory recursively. Defaults to False.", "optional": true } }
- Schema:
def estimate_resources_for_gnn_directory_mcp(dir_path, recursive)(AST parsed) - *"Estimate resources for all GNN files in a directory. Exposed via MCP."def estimate_resources_for_gnn_file(file_path)- *Description: "Estimates computational resources (memory, inference, storage) for a GNN model file."- Schema:
json { "file_path": { "type": "string", "description": "Path to the GNN file for resource estimation." } }
- Schema:
def estimate_resources_for_gnn_file_mcp(file_path)(AST parsed) - *"Estimate computational resources for a single GNN file. Exposed via MCP."def register_tools(mcp_instance)(AST parsed) - *"Register GNN type checker and resource estimator tools with the MCP."def type_check_gnn_directory(dir_path, recursive, output_dir_base, report_md_filename)- *Description: "Runs the GNN type checker on all GNN files in a specified directory. If output_dir_base is provided, reports are generated."- Schema:
json { "dir_path": { "type": "string", "description": "Path to the directory containing GNN files to be type-checked." }, "recursive": { "type": "boolean", "description": "Search directory recursively. Defaults to False.", "optional": true }, "output_dir_base": { "type": "string", "description": "Optional base directory to save the report and other artifacts (HTML, JSON).", "optional": true }, "report_md_filename": { "type": "string", "description": "Optional filename for the markdown report (e.g., 'my_report.md'). Defaults to 'type_check_report.md'.", "optional": true } }
- Schema:
def type_check_gnn_directory_mcp(dir_path, recursive, output_dir_base, report_md_filename)(AST parsed) - *"Run the GNN type checker on all GNN files in a directory. Exposed via MCP."def type_check_gnn_file(file_path)- *Description: "Runs the GNN type checker on a specified GNN model file."- Schema:
json { "file_path": { "type": "string", "description": "Path to the GNN file to be type-checked." } }
- Schema:
def type_check_gnn_file_mcp(file_path)(AST parsed) - *"Run the GNN type checker on a single GNN file. Exposed via MCP."
Module: ontology (at /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/ontology)
- ✅
mcp.pyStatus: Found (13473 bytes) - Exposed Methods & Tools:
def generate_ontology_report_for_file(gnn_file_path, parsed_annotations, validation_results)(AST parsed) - *"Generates a markdown formatted report string for a single GNN file's ontology annotations."def get_mcp_interface()(AST parsed) - *"Returns the MCP interface for the Ontology module."def load_defined_ontology_terms(ontology_terms_path, verbose)(AST parsed) - *"Loads defined ontological terms from a JSON file."def parse_gnn_ontology_section(gnn_file_content, verbose)(AST parsed) - *"Parses the 'ActInfOntologyAnnotation' section from GNN file content."def validate_annotations(parsed_annotations, defined_terms, verbose)(AST parsed) - *"Validates parsed GNN annotations against a set of defined ontological terms."
Module: setup (at /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/setup)
- ✅
mcp.pyStatus: Found (4257 bytes) - Exposed Methods & Tools:
def ensure_directory_exists(directory_path)- *Description: "Ensures a directory exists, creating it if necessary. Returns the absolute path."- Schema:
json { "directory_path": { "type": "string", "description": "Path of the directory to create if it doesn't exist." } }
- Schema:
def ensure_directory_exists_mcp(directory_path)(AST parsed) - *"Ensure a directory exists, creating it if necessary. Exposed via MCP."def find_project_gnn_files(search_directory, recursive)- *Description: "Finds all GNN (.md) files in a specified directory within the project."- Schema:
json { "search_directory": { "type": "string", "description": "The directory to search for GNN (.md) files." }, "recursive": { "type": "boolean", "description": "Set to true to search recursively. Defaults to false.", "optional": true } }
- Schema:
def find_project_gnn_files_mcp(search_directory, recursive)(AST parsed) - *"Find all GNN (.md) files in a directory. Exposed via MCP."def get_standard_output_paths(base_output_directory)- *Description: "Gets a dictionary of standard output directory paths (e.g., for type_check, visualization), creating them if needed."- Schema:
json { "base_output_directory": { "type": "string", "description": "The base directory where output subdirectories will be managed." } }
- Schema:
def get_standard_output_paths_mcp(base_output_directory)(AST parsed) - *"Get standard output paths for the pipeline. Exposed via MCP."def register_tools(mcp_instance)(AST parsed) - *"Register setup utility tools with the MCP."
Module: tests (at /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/tests)
- ✅
mcp.pyStatus: Found (7083 bytes) - Exposed Methods & Tools:
def get_test_report(uri)(AST parsed) - *"Retrieve a test report by URI."def register_tools(mcp)(AST parsed) - *"Register test tools with the MCP."def run_gnn_type_checker(file_path)- *Description: "Run the GNN type checker on a specific file (via test module)."- Schema:
json { "file_path": { "type": "string", "description": "Path to the GNN file to check" } }
- Schema:
def run_gnn_type_checker_on_directory(dir_path, report_file)- *Description: "Run the GNN type checker on all GNN files in a directory (via test module)."- Schema:
json { "dir_path": { "type": "string", "description": "Path to directory containing GNN files" }, "report_file": { "type": "string", "description": "Optional path to save the report" } }
- Schema:
def run_gnn_unit_tests()- *Description: "Run the GNN unit tests and return results."def run_type_checker_on_directory(dir_path, report_file)(AST parsed) - *"Run the GNN type checker on a directory of files."def run_type_checker_on_file(file_path)(AST parsed) - *"Run the GNN type checker on a file."def run_unit_tests()(AST parsed) - *"Run the GNN unit tests."
Module: visualization (at /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/visualization)
- ✅
mcp.pyStatus: Found (5934 bytes) - Exposed Methods & Tools:
def get_visualization_results(uri)(AST parsed) - *"Retrieve visualization results by URI."def parse_gnn_file(file_path)- *Description: "Parse a GNN file without visualization"- Schema:
json { "file_path": { "type": "string", "description": "Path to the GNN file to parse" } }
- Schema:
def register_tools(mcp)(AST parsed) - *"Register visualization tools with the MCP."def visualize_directory(dir_path, output_dir)(AST parsed) - *"Visualize all GNN files in a directory through MCP."def visualize_file(file_path, output_dir)(AST parsed) - *"Visualize a GNN file through MCP."def visualize_gnn_directory(dir_path, output_dir)- *Description: "Visualize all GNN files in a directory"- Schema:
json { "dir_path": { "type": "string", "description": "Path to directory containing GNN files" }, "output_dir": { "type": "string", "description": "Optional output directory" } }
- Schema:
def visualize_gnn_file(file_path, output_dir)- *Description: "Generate visualizations for a specific GNN file."- Schema:
json { "file_path": { "type": "string", "description": "Path to the GNN file to visualize" }, "output_dir": { "type": "string", "description": "Optional output directory" } }
- Schema:
Module: llm (at /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/llm)
- ✅
mcp.pyStatus: Found (19238 bytes) - Exposed Methods & Tools:
def ensure_llm_tools_registered(mcp_instance_ref)(AST parsed) - *"Ensures that LLM tools are registered with the provided MCP instance."def explain_gnn_file_content(file_path_str, aspect_to_explain)(AST parsed) - *"Reads a GNN file, sends its content to an LLM, and returns an explanation."def generate_professional_summary_from_gnn(file_path_str, experiment_details, target_audience)(AST parsed) - *"Generates a professional summary of a GNN model and its experimental context."def initialize_llm_module(mcp_instance_ref)(AST parsed) - *"Initializes the LLM module, loads API key, and updates MCP status."def llm.explain_gnn_file(file_path_str, aspect_to_explain)- *Description: "Reads a GNN specification file and uses an LLM to generate an explanation of its content. Can focus on a specific aspect if provided."- Schema:
json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file." }, "aspect_to_explain": { "type": "string", "description": "(Optional) A specific part or concept within the GNN to focus the explanation on." } }, "required": [ "file_path_str" ] }
- Schema:
def llm.generate_professional_summary(file_path_str, experiment_details, target_audience)- *Description: "Reads a GNN file and optional experiment details, then uses an LLM to generate a professional summary suitable for reports or papers."- Schema:
json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file." }, "experiment_details": { "type": "string", "description": "(Optional) Text describing the experiments conducted with the model, including setup, results, or observations." }, "target_audience": { "type": "string", "description": "(Optional) The intended audience for the summary (e.g., 'fellow researchers', 'project managers'). Default: 'fellow researchers'." } }, "required": [ "file_path_str" ] }
- Schema:
def llm.summarize_gnn_file(file_path_str, user_prompt_suffix)- *Description: "Reads a GNN specification file and uses an LLM to generate a concise summary of its content. Optionally, a user prompt suffix can refine the summary focus."- Schema:
json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file (.md, .gnn.md, .json)." }, "user_prompt_suffix": { "type": "string", "description": "(Optional) Additional instructions or focus points for the summary." } }, "required": [ "file_path_str" ] }
- Schema:
def register_tools(mcp_instance_ref)(AST parsed)def summarize_gnn_file_content(file_path_str, user_prompt_suffix)(AST parsed) - *"Reads a GNN file, sends its content to an LLM, and returns a summary."
📊 Overall Module Integration Summary
- Modules Checked: 8
mcp.pyIntegrations Found: 8/8- Status: All expected functional modules appear to have an
mcp.pyintegration file. Please ensure each functional module that should be exposed via MCP has its ownmcp.pyfollowing the project's MCP architecture.
Ontology Processing (Step 8)
🧬 GNN Ontological Annotations Report
📊 Summary of Ontology Processing
- Files Processed: 2 / 2
- Total Ontological Annotations Found: 32
- Total Annotations Validated: 32
- ✅ Passed: 20
- ❌ Failed: 12
��️ Report Generated: 2025-06-07 12:29:09
🎯 GNN Source Directory: src/gnn/examples
📖 Ontology Terms Definition: src/ontology/act_inf_ontology_terms.json (Loaded: 48 terms)
Ontological Annotations for src/gnn/examples/pymdp_pomdp_agent.md
Mappings:
A_m0->LikelihoodMatrixModality0A_m1->LikelihoodMatrixModality1A_m2->LikelihoodMatrixModality2B_f0->TransitionMatrixFactor0B_f1->TransitionMatrixFactor1C_m0->LogPreferenceVectorModality0C_m1->LogPreferenceVectorModality1C_m2->LogPreferenceVectorModality2D_f0->PriorOverHiddenStatesFactor0D_f1->PriorOverHiddenStatesFactor1s_f0->HiddenStateFactor0s_f1->HiddenStateFactor1s_prime_f0->NextHiddenStateFactor0s_prime_f1->NextHiddenStateFactor1o_m0->ObservationModality0o_m1->ObservationModality1o_m2->ObservationModality2π_f1->PolicyVectorFactor1u_f1->ActionFactor1G->ExpectedFreeEnergy
Validation Summary: All ontological terms are recognized.
Ontological Annotations for src/gnn/examples/rxinfer_multiagent_gnn.md
Mappings:
dt->TimeStep(INVALID TERM)gamma->ConstraintParameter(INVALID TERM)nr_steps->TrajectoryLength(INVALID TERM)nr_iterations->InferenceIterations(INVALID TERM)nr_agents->NumberOfAgents(INVALID TERM)softmin_temperature->SoftminTemperature(INVALID TERM)A->StateTransitionMatrix(INVALID TERM)B->ControlInputMatrix(INVALID TERM)C->ObservationMatrix(INVALID TERM)initial_state_variance->InitialStateVariance(INVALID TERM)control_variance->ControlVariance(INVALID TERM)goal_constraint_variance->GoalConstraintVariance(INVALID TERM)
Validation Summary: 12 unrecognized ontological term(s) found.
Rendered Simulators (Step 9)
Simulators for rxinfer_toml: rxinfer_toml
Other Files
LLM Processing Outputs (Step 11)
LLM Outputs for pymdp_pomdp_agent: pymdp_pomdp_agent
JSON Files
pymdp_pomdp_agent_comprehensive_analysis.json
{
"model_purpose": "The model represents a Multifactor PyMDP agent that operates using multiple observation modalities and hidden state factors, aimed at decision-making under uncertainty through active inference.",
"key_components": {
"hidden_states": {
"reward_level": {
"states": 2,
"description": "Represents the level of reward the agent perceives."
},
"decision_state": {
"states": 3,
"description": "Represents the internal state related to the agent's decision-making process."
}
},
"observations": {
"state_observation": {
"outcomes": 3,
"description": "Observations related to the current state of the environment."
},
"reward": {
"outcomes": 3,
"description": "Observations related to the received reward."
},
"decision_proprioceptive": {
"outcomes": 3,
"description": "Observations that reflect the agent's own decision processes."
}
},
"actions": {
"decision_state_actions": {
"possible_actions": 3,
"description": "Actions that can be taken to influence the decision state."
}
},
"policy": {
"policy_distribution": {
"description": "A distribution over actions for the controllable decision state."
}
},
"parameters": {
"A_matrices": "Likelihood matrices for observations.",
"B_matrices": "Transition matrices for hidden state factors.",
"C_vectors": "Preference vectors for observations.",
"D_vectors": "Prior distributions over hidden states."
}
},
"component_interactions": {
"hidden_states": "(D_f0,D_f1)-(s_f0,s_f1)",
"observations": "(s_f0,s_f1)-(A_m0,A_m1,A_m2)",
"likelihoods": "(A_m0,A_m1,A_m2)-(o_m0,o_m1,o_m2)",
"transitions": "(s_f0,s_f1,u_f1)-(B_f0,B_f1)",
"next_states": "(B_f0,B_f1)-(s_prime_f0,s_prime_f1)",
"policy": "(C_m0,C_m1,C_m2)>G",
"expected_free_energy": "G>\u03c0_f1",
"action": "\u03c0_f1-u_f1"
},
"data_types_and_dimensions": {
"A_matrices": {
"type": "float",
"dimensions": {
"A_m0": "[3,2,3]",
"A_m1": "[3,2,3]",
"A_m2": "[3,2,3]"
}
},
"B_matrices": {
"type": "float",
"dimensions": {
"B_f0": "[2,2,1]",
"B_f1": "[3,3,3]"
}
},
"C_vectors": {
"type": "float",
"dimensions": {
"C_m0": "[3]",
"C_m1": "[3]",
"C_m2": "[3]"
}
},
"D_vectors": {
"type": "float",
"dimensions": {
"D_f0": "[2]",
"D_f1": "[3]"
}
},
"hidden_states": {
"s_f0": "[2,1]",
"s_f1": "[3,1]",
"s_prime_f0": "[2,1]",
"s_prime_f1": "[3,1]"
},
"observations": {
"o_m0": "[3,1]",
"o_m1": "[3,1]",
"o_m2": "[3,1]"
},
"policy": {
"\u03c0_f1": "[3]",
"u_f1": "[1]",
"G": "[1]",
"t": "[1]"
}
},
"potential_applications": [
"Decision-making in uncertain environments",
"Robotics and autonomous systems",
"Reinforcement learning scenarios",
"Psychological modeling of decision processes"
],
"limitations_or_ambiguities": [
"The model does not specify the exact dynamics of how actions affect the hidden states, particularly for the uncontrolled factor.",
"The initial parameterization may need empirical tuning for specific applications.",
"The unbounded time horizon could complicate practical implementations."
],
"ontology_mapping_assessment": {
"ActInfOntology_terms": {
"A_m0": "LikelihoodMatrixModality0",
"A_m1": "LikelihoodMatrixModality1",
"A_m2": "LikelihoodMatrixModality2",
"B_f0": "TransitionMatrixFactor0",
"B_f1": "TransitionMatrixFactor1",
"C_m0": "LogPreferenceVectorModality0",
"C_m1": "LogPreferenceVectorModality1",
"C_m2": "LogPreferenceVectorModality2",
"D_f0": "PriorOverHiddenStatesFactor0",
"D_f1": "PriorOverHiddenStatesFactor1",
"s_f0": "HiddenStateFactor0",
"s_f1": "HiddenStateFactor1",
"s_prime_f0": "NextHiddenStateFactor0",
"s_prime_f1": "NextHiddenStateFactor1",
"o_m0": "ObservationModality0",
"o_m1": "ObservationModality1",
"o_m2": "ObservationModality2",
"\u03c0_f1": "PolicyVectorFactor1",
"u_f1": "ActionFactor1",
"G": "ExpectedFreeEnergy"
},
"assessment": "All relevant ActInfOntology terms are present and appropriately mapped to the model components."
}
}pymdp_pomdp_agent_qa.json
[
{
"question": "What are the implications of having multiple observation modalities on the decision-making process of the Multifactor PyMDP Agent?",
"answer": "The GNN file indicates that the Multifactor PyMDP Agent utilizes multiple observation modalities\u2014specifically \"state_observation,\" \"reward,\" and \"decision_proprioceptive\"\u2014each with three outcomes. The implications of having these multiple modalities on the decision-making process include:\n\n1. **Enhanced Information Capture**: The agent can gather diverse types of information from the environment, which can lead to a more comprehensive understanding of the current state and context.\n\n2. **Improved State Inference**: With multiple modalities, the agent can potentially increase the accuracy of state inference, as different modalities may provide complementary information that helps in resolving ambiguities in hidden states.\n\n3. **Complex Decision Policies**: The presence of multiple observations allows for more complex decision-making policies, as the agent can weigh the preferences and likelihoods derived from each modality when determining actions.\n\n4. **Adaptability**: The agent can adapt its behavior based on the specific information available from each modality, allowing for a more flexible response to varying situations.\n\nOverall, multiple observation modalities enhance the agent's decision-making by providing richer data for state estimation and action selection."
},
{
"question": "How does the control over the 'decision_state' factor influence the overall performance and adaptability of the agent in varying environments?",
"answer": "The GNN file does not provide sufficient information to directly answer how control over the 'decision_state' factor influences the overall performance and adaptability of the agent in varying environments. While it mentions that the 'decision_state' factor is controllable with three possible actions, it lacks specific details on how these actions impact performance and adaptability in different scenarios."
},
{
"question": "In what ways do the defined likelihood matrices (A_m) and transition matrices (B_f) affect the agent's ability to infer states and select actions?",
"answer": "The defined likelihood matrices (A_m) and transition matrices (B_f) play crucial roles in the agent's ability to infer states and select actions:\n\n1. **Likelihood Matrices (A_m)**:\n - The A_m matrices define the probabilities of observing certain outcomes given specific hidden states. Each modality (A_m0, A_m1, A_m2) influences how the agent updates its beliefs about the hidden states (s_f0 and s_f1) based on observed data (o_m0, o_m1, o_m2).\n - The agent uses these likelihoods to infer the most probable hidden states from the observations, enhancing its ability to accurately model the environment and make predictions.\n\n2. **Transition Matrices (B_f)**:\n - The B_f matrices govern the dynamics of the hidden states over time. For factor 0 (B_f0), the transitions are uncontrolled, meaning they are independent of actions. For factor 1 (B_f1), transitions depend on the chosen actions (u_f1), allowing the agent to influence its state evolution based on decisions.\n - This structure enables the agent to select actions that not only influence the next state but also reflect the agent's understanding of the environment, thus directly impacting its policy inference and action selection.\n\nTogether, these matrices facilitate the agent's ability to integrate sensory information (via A_m) and manage state transitions (via B_f), which are essential for effective state inference and action selection."
},
{
"question": "What assumptions are made regarding the independence of hidden state factors in this model, and how might these assumptions impact the agent's learning and decision-making?",
"answer": "The GNN file content does not explicitly state any assumptions regarding the independence of hidden state factors in the model. However, the structure implies that the hidden state factors \"reward_level\" (s_f0) and \"decision_state\" (s_f1) are treated separately in the transition matrices (B_f0 and B_f1) and observation likelihoods (A_m matrices). \n\nThis treatment suggests an assumption of conditional independence between the hidden state factors given the actions and observations. Such an assumption could simplify the modeling process but might limit the agent's ability to learn complex dependencies between the hidden states. If the hidden states are indeed interdependent in reality, this assumption could lead to suboptimal learning and decision-making, as the agent may fail to capture important interactions that influence its performance in a dynamic environment. Thus, the independence assumption may impact the agent's ability to make informed decisions based on the full context of its hidden states."
},
{
"question": "How does the structure of the initial parameterization affect the convergence and stability of the agent's learning process during simulations?",
"answer": "The GNN file does not contain sufficient information to directly assess how the structure of the initial parameterization affects the convergence and stability of the agent's learning process during simulations. The document provides details about the parameterization itself, such as the likelihood matrices, transition matrices, and preferences, but it lacks specific insights or empirical data regarding their impact on learning dynamics or stability. Therefore, I cannot provide a definitive answer to the question based solely on the provided GNN file content."
}
]Text/Log Files
pymdp_pomdp_agent_summary.txt
### Summary of the Multifactor PyMDP Agent GNN Model
**Model Name:** Multifactor PyMDP Agent v1
**Purpose:** This model represents a Generalized Notation Notation (GNN) framework for a PyMDP agent that integrates multiple observation modalities and hidden state factors to facilitate decision-making in an Active Inference paradigm.
**Key Components:**
1. **Observation Modalities:**
- **State Observation:** 3 outcomes
- **Reward:** 3 outcomes
- **Decision Proprioceptive:** 3 outcomes
2. **Hidden State Factors:**
- **Reward Level:** 2 states
- **Decision State:** 3 states
3. **Control:**
- The **decision_state** factor is controllable with 3 possible actions.
**Main Connections:**
- The model's structure includes connections between the hidden states and observation modalities, with the following key interactions:
- Hidden states (s_f0 and s_f1) influence the likelihood matrices (A_m0, A_m1, A_m2) for different observation modalities.
- The control factor (u_f1) primarily affects the transition matrix (B_f1) for the decision state while B_f0 remains uncontrolled.
- Observations feed into the expected free energy (G), which in turn influences the policy distribution (π_f1) for decision-making.
**Overall Structure:**
The model is designed for dynamic and discrete-time processes, aiming to infer the states based on observations, derive policies, and sample actions to optimize decision-making based on expected free energy. It leverages the mathematical representations of states, observations, and transitions to maintain a coherent decision-making framework.LLM Outputs for rxinfer_multiagent_gnn: rxinfer_multiagent_gnn
JSON Files
rxinfer_multiagent_gnn_comprehensive_analysis.json
{
"model_purpose": "This GNN model is designed for multi-agent trajectory planning using the RxInfer.jl framework. It aims to simulate and optimize the trajectories of multiple agents in a 2D environment while considering obstacle avoidance, goal-directed behavior, and collision avoidance among agents.",
"key_components": {
"state_space_model": {
"description": "Defines the dynamics of the agents in the environment.",
"parameters": {
"A": "State transition matrix",
"B": "Control input matrix",
"C": "Observation matrix",
"dt": "Time step for state updates"
}
},
"agent_trajectories": {
"description": "Tracks the predicted paths of agents over time.",
"parameters": {
"initial_state_variance": "Variance in the initial position of agents",
"control_variance": "Variance in control inputs applied to agents",
"goal_constraint_variance": "Variance in the constraints related to agent goals"
}
},
"obstacle_avoidance": {
"description": "Ensures agents avoid obstacles in their paths.",
"parameters": {
"gamma": "Constraint parameter for proximity to obstacles",
"gamma_shape": "Shape parameter for prior distribution on distances",
"gamma_scale_factor": "Scale factor for the distribution"
}
},
"collision_avoidance": {
"description": "Manages the interactions between agents to prevent collisions.",
"parameters": {
"nr_agents": "Number of agents interacting"
}
},
"goal_directed_behavior": {
"description": "Directs agents towards their specific targets."
}
},
"component_interactions": {
"state_space_model": "Feeds into agent trajectories to predict their paths.",
"agent_trajectories": "Interacts with goal constraints and obstacle avoidance to optimize paths.",
"goal_constraints": "Informs agents of their target positions.",
"obstacle_avoidance": "Uses information about agent trajectories to enforce avoidance of obstacles.",
"collision_avoidance": "Utilizes the positions of agents to prevent overlaps during trajectory planning.",
"planning_system": "Combines goal-directed behavior, obstacle avoidance, and collision avoidance to create a complete trajectory planning solution."
},
"data_types_and_dimensions": {
"parameters": {
"dt": "float",
"gamma": "float",
"nr_steps": "int",
"nr_iterations": "int",
"nr_agents": "int",
"softmin_temperature": "float",
"intermediate_steps": "int",
"save_intermediates": "bool",
"initial_state_variance": "float",
"control_variance": "float",
"goal_constraint_variance": "float",
"gamma_shape": "float",
"gamma_scale_factor": "float",
"x_limits": "float[2]",
"y_limits": "float[2]",
"fps": "int",
"heatmap_resolution": "int",
"plot_width": "int",
"plot_height": "int",
"agent_alpha": "float",
"target_alpha": "float",
"color_palette": "string",
"obstacles": "float[2]",
"agent_id": "int",
"agent_radius": "float",
"agent_initial_position": "float[2]",
"agent_target_position": "float[2]",
"experiment_seeds": "int[2]",
"results_dir": "string",
"animation_template": "string",
"filenames": "string"
},
"matrices": {
"A": "float[4][4]",
"B": "float[4][2]",
"C": "float[2][4]"
}
},
"potential_applications": [
"Robotics: Planning the movement of multiple robots in shared environments.",
"Autonomous Vehicles: Managing the trajectory of multiple vehicles to avoid collisions.",
"Simulation: Testing algorithms for multi-agent coordination in complex environments."
],
"limitations_or_ambiguities": [
"The model assumes a simplified 2D space which might not capture all real-world complexities.",
"Parameter tuning for variances and constraints may require empirical testing to optimize performance.",
"The representation does not specify the nature of the control inputs, which could affect agent behaviors."
],
"ontology_mapping_assessment": {
"terms_present": [
"TimeStep",
"ConstraintParameter",
"TrajectoryLength",
"InferenceIterations",
"NumberOfAgents",
"SoftminTemperature",
"StateTransitionMatrix",
"ControlInputMatrix",
"ObservationMatrix",
"InitialStateVariance",
"ControlVariance",
"GoalConstraintVariance"
],
"relevance": "The ActInfOntology terms are well-defined and applicable for the model parameters, ensuring a clear mapping to the ontology which enhances the comprehensibility and utility of the GNN representation."
}
}Text/Log Files
rxinfer_multiagent_gnn_summary.txt
### Summary of the GNN Model: Multi-agent Trajectory Planning
**Model Name:** Multi-agent Trajectory Planning
**Purpose:** This GNN model represents a multi-agent trajectory planning scenario designed for use with RxInfer.jl. It aims to simulate how multiple agents navigate a 2D environment while adhering to various constraints, including obstacle avoidance and collision avoidance, to reach designated targets.
**Key Components:**
1. **State Space Model:**
- The model incorporates a state space representation for agents moving in a 2D environment, defined by:
- **State Transition Matrix (A)**: Determines how agents transition between states over time.
- **Control Input Matrix (B)**: Maps control inputs to state changes.
- **Observation Matrix (C)**: Relates the state of agents to observable outputs.
2. **Primary States/Observations:**
- Agents' positions and velocities are represented in a state vector, with observations influenced by noise characterized by defined variances for initial state, control inputs, and goal constraints.
3. **Main Connections:**
- **Agent Trajectories**: The state space model outputs trajectories based on initial states and control variances.
- **Goal Constraints**: Trajectories are adjusted to consider proximity to targets.
- **Obstacle Avoidance**: Constraints are applied based on the distances to obstacles, ensuring agents avoid collisions with them.
- **Collision Avoidance**: Interactions between agents are managed to prevent overlapping trajectories, considering their respective sizes.
- **Planning System**: Combines goal-directed behavior, obstacle avoidance, and collision avoidance into a cohesive planning framework.
This model serves as a comprehensive framework for simulating and analyzing the complex interactions between multiple agents in dynamic environments while ensuring their safe navigation towards goals.Pipeline Log
Other Output Files/Directories
- directory_structure.json
- discopy_gnn/ (Directory - Browse)
- installed_packages.json
- visualization/ (Directory - Browse)